Open3D 0.16 Release Notes

The fall brings a new "tock" release of Open3D, packed with new features and updates! Here are the highlights:

• Open3D visualization now works on Google Colab, and CPU rendering is now easy to use on all Linux machines.
• The visualizer has a new wireframe mode.
• Many new geometry processing algorithms were added, including mesh clipping, hole filling, extrusion and generation of text meshes.
• Added support for texture baking to create great looking 3D models.
• The Open3D standalone viewer is now available for Apple Silicon.
• Dependency version updates: Python 3.10, Tensorflow 2.8, PyTorch 1.12.

Watch the release video here.

Google Summer of Code

Open3D had a successful Google Season of Code 2022 with many new features added, and more in the works for the next release. Here are the features that are part of this release:

Geometry processing algorithms for tensor-based geometry [Contributed by @ZhengyuDiao]

• [new] Geometry creation functions: create_arrow(), create_box(), create_cone(), create_coordinate_frame(), create_cylinder(), create_icosahedron(), create_mobius(), create_octahedron(), create_sphere(), create_tetrahedron(), create_torus().
• [update] Improved support for reading / writing text format point cloud files in XYZ, XYZN, XYZI and XYZRGB formats.
• [fix] Fix thepng/jpg textures loading in glb (binary glTF) file.
• [update] Tensor point cloud IO now uses float instead of double by default.

Open3D visualization on Google Colab [Contributed by @FromBei-JingWithLove]

• [new] draw_plotly method brings interactive 3D visualization for Open3D to Jupyter notebooks and cloud environments (e.g., Colab).
• [new] Tutorial notebook demonstrating the various ways to use draw_plotly

Build System

• [new] Open3D is now available for Python 3.10. Python 3.6 is no longer supported.
• [new] C++ users can now link to the Open3D dynamic library through pkg-config files. There are available for Linux and macOS in the Open3D binary packages.
• [new] Alpha support for SYCL devices (AlderLake integrated GPUs, Arc GPUs).
• [fix] Windows and Visual Studio build fixes (contributed by @nigels-com, @jmherzog-de, @yuecideng)
• [fix] Fixed build Error on Fedora 36 with GCC 12 (contributed by @NobuoTsukamoto)
• [fix] Fix link error during build due to invalid LIBDIR in newer Debian systems (contributed by @bchretien)
• [update] Improved support for building on Apple Silicon.
• [update] Separate Python build (requirements_build.txt) and runtime (requirements.txt) dependencies. (with help from @johnthagen).
• [update] Documentation can be built faster with the make_docs.py --parallel option.

Core

• [new] Enable pickling for tensor and tensor based geometry (contributed by @yuecideng).
• [new] Random seed Open3D globally. Utility random number generation functions to ensure the global Open3D random seed is used.

  import open3d as o3d
  o3d.utility.random.seed(42)
  

  #include "open3d/utility/Random.h"
  int main() {
      using namespace open3d;
      utility::random::Seed(42);  // Globally seed.
      std::cout << utility::random::RandUint32() << std::endl;  // Simply get a random number.
      return 0;
  }
  

• [update] Faster creation of many small Tensors by using statically allocated memory for SizeVector.
• [new] Add tensor minimum(), maximum() ops (contributed by @yuecideng).
• [new] Add tensor Any(), All(), RemoveNonFinite()
• [update] Support for int64 index dtype in NearestNeighborSearch (contributed bu @chrockey)
• [update] The new datasets API is used throughout Open3D, including pipelines, examples and unit tests. New models with PBR materials (Helmet, Jesse monkey, crate and sword) are available. Add redwood indoor datasets (Augmented ICL-NUIM Dataset).
• [update] Support RadiusSearch for EstimateNormals() for Tensor PointCloud (contributed by @yuecideng).

Geometry

• [update] Tensor geometry attributes can now be accessed by the easier dot notation, in addition to the previous dictionary notation. For example:

pcd.point["colors"] is pcd.point.colors
tmesh.triangle["normals"] is tmesh.triangle.normals

Geometry classes of the Tensor API also add new functionality based on VTK:

• Mesh clipping along a plane
• Extraction of slices from triangle meshes as line sets
• Hole filling
• Boolean operations between watertight meshes
• A new algorithm for quadric decimation of triangle meshes
• Linear and rotational extrusion
• Generation of text meshes

Further, we add functionality for parametrising meshes with the UVAtlas library and added functions for baking vertex and triangle attributes to textures.

New operations for Tensor PointCloud:

• ClusterDBScan, ConvexHull
• Radius Outlier Filter, RemoveDuplicatedPoints, PaintUniformColor
• FarthestPointDownSample, HiddenPointRemoval and SegmentPlane
• Uniform and random down sample methods (contributed by @yuecideng).
• FPFH features. (contributed by @yuecideng)
• PointCloud boundary detection (contributed by @yuecideng)

PointCloud | Boundaries :--------------------:|:--------------------: |

• [new] RemoveDuplicatedPoints() for PointCloud (Eigen API). (contributed by @scimad)
• [new] Farthest point downsampling (Eigen API) (contributed by @yuecideng).
• [update] Plane segmentation in point clouds is now multi-threaded. (Eigen API) (contributed by @yuecideng).
• [update] Add SelectByIndex and minor improvement to SelectByMask (contributed by @yuecideng).
• [new] Add Tensor AxisAlignedBoundingBox (contributed by @yuecideng).
• [update] SelectPolygonVolume.CropInPolygon returns point indices.
• [fix] Blocky / incomplete raycast output for larger voxel sizes fixed with EstimateRange() update (contributed by @jdavidberger).
• [update] Allow setting of number of threads that are used for building a raycasting scene (contributed by @jjabo)

Visualization

• [new] Wireframe mode added to Open3D visualizer
• [new] Support for Filament's bloom post-processing effects
• [fix] Make sure float and uint16 texture formats work correctly
• [fix] Fix bugs with point_width and line_width parameters in Material class

CPU rendering

• [update] Easy to use out of the box CPU rendering for Linux. Pre-built Mesa CPU rendering library is provided in the Python wheel if the system Mesa library is too old or not in use. You can enable CPU rendering with the environment variable OPEN3D_CPU_RENDERING=true before importing Open3D in Python or running a C++ program. See the tutorial for full details.
• [new] Updated CPU rendering works in Jupyter and Colab environments.

GUI

• [update] Vertical and horizontal radio button groups. (contributed by @forrestjgq)
• [new] Keypress event callback for windows. (contributed by @forrestjgq)
• [fix] Fix mouse event filter issue in popup menu covered area (contributed by @forrestjgq)
• [new] Added extra VisualizerWithVertexSelection point picking functions to python API (contributed by @d-walsh and @cansik)
• [update] Support double click mouse event in WebRTC. (contributed by @forrestjgq)
• [fix] Fix Point Clouds Getting Culled Prematurely.
• [fix] Set correct default material (lit with flat shading) for triangle mesh with triangle normals.
• [update] Add interface for get cropped geometry from VisualizerWithEditing class (contributed by @yuecideng).

IO

• [new] Open3D now implements a distortion and noise model from the Redwood dataset. This will enable you to simulate the output of realistic depth sensors.

Original | With noise + distortion | Difference :-------------------------:|:-------------------------:|:---------------------: | |

• [fix] Fix gltf PBR model load regression when loading through ASSIMP.
• [new] Read ply, stl, obj, off, gltf, glb, fbx file formats directly into a Tensor TriangleMesh.

Reconstruction

• [update] Update ICP interface to get iteration wise loss information.
• [new] Online SLAM Example with Realsense sensor support, bagfile support, and saved dataset support.
• [new] Legacy offline reconstruction C++ pipeline (contributed by @yuecideng)
• [update] InitializePointCloudForColoredICP efficiency improvement. (contributed by @Xiang-Zeng)

Open3D-ML

• [update] Upgrade PyTorch to v1.12 and TensorFlow to v2.8.2. With the new PyTorch version, Open3D PyTorch ops work directly with standard PyTorch wheels and custom wheels are not required any more.
• [fix] Avoid logging.basicConfig in non-scripts (contributed by @ajprax)
• [fix] filter_valid_label: force scores tensor onto the selected device (contributed by @ntw-au)
• [fix] Ensure that a checkpoint is saved on the final training epoch (contributed by @ntw-au)
• [fix] Handle nested dictionaries when merging configs (contributed by @ntw-au)
• [fix] Fix moving zip files (contributed by @tejaswid)
• [fix] Update RandLANet weights link (contributed by @Krupal09)
• [fix] Getting rid of torch._six (for future PyTorch versions) (contributed by @krshrimali)

Acknowledgments

We would like to thank all of our community contributors for their true labor of love for this release!

@ntw-au, @jdavidberger, @Xiang-Zeng, @jamesdi1993, @brentyi, @jjabo, @jbotsch-fy, @scimad, @cansik, @NobuoTsukamoto, @theNded, @chunibyo-wly, @jmherzog-de, @luzpaz, @code-review-doctor, @d-walsh, @johnthagen, @pmokeev, @erbensley, @hanzheteng, @chrockey, @agrellRepli, @bchretien, @nigels-com, @forrestjgq, @equant, @naruarjun, @ajprax, @INF800, @ntw-au, @tejaswid, @Krupal09, @krshrimali

Also thanks to the many others who helped the Open3D community by reporting as well as resolving issues.

Open3D 0.15 Release Notes

We are excited to bring you the best Open3D yet - version 0.15. Watch the release video here.

Starting from this release, we adopt a "tick-tock" model for balancing resolving issues vs. adding new features. In a nutshell, the "tick" releases are focused on resolving existing issues and eliminating bugs, while the "tock" releases mainly focus on developing new features. Open3D 0.15 is a "tick" release. We resolved over 500 issues for Open3D and Open3D-ML, as the infographic below illustrates.

Google Summer of Code 2022

Open3D has applied for the Google Summer of Code 2022 to increase community participation. Check out details and our project ideas here. Please help in making Open3D better for all.

Build System

• [New] We now provide Open3D binary packages for C++ users. No need to “Build from Source” - just download a binary package for your Operating System and use it “out of the box”. See GitHub releases for v0.15 and getting started guides for the latest development package.
• [New] Docker build tools to build ARM64 Linux wheels and multiple Python versions. See ARM64 build guide for more details.
• [New] Pre-compiled Open3D wheel for ARM64 Linux and macOS. Improved Apple Silicon support. Install Open3D on ARM64 Linux and macOS with pip install open3d.
• [Update] Open3D now builds with the new CXX11 ABI by default on Linux. Set -DGLIBCXX_USE_CXX11_ABI=OFF in cmake if you need the old ABI, e.g. to work with PyTorch / TensorFlow libraries.
• [Update] Starting with version 0.15, Open3D Conda packages are no longer supported. Install Open3D with pip install open3d inside a Conda virtual environment.

Core

Datasets

• [New] Dataset module for automatically downloading and managing example data. The following example demonstrates how to create a Dataset object, extract its path, and display it in the Open3D Visualizer:

  import open3d as o3d
  
  if __name__ == "__main__":
      dataset = o3d.data.EaglePointCloud()
      pcd = o3d.io.read_point_cloud(dataset.path)
      o3d.visualization.draw(pcd)
  

  #include <string>
  #include <memory>
  #include "open3d/Open3D.h"
  
  int main() {
      using namespace open3d;
  
      data::EaglePointCloud dataset;
      auto pcd = io::CreatePointCloudFromFile(dataset.GetPath());
      visualization::Draw({pcd});
  
      return 0;
  }
  

Command Line Interface (CLI)

• [New] Open3D-dedicated Command Line Interface (CLI) for visualization and running Python examples. Below is a code snippet to get started with Open3D and its examples.

  # Install Open3D pip package
  pip install open3d
  
  # Print help
  open3d --help
  
  # List all runnable examples
  open3d example --list
  
  # Print source code of an example
  open3d example --show [category]/[example_name]
  
  # Run an example
  open3d example [category]/[example_name]
  
  # Run Open3D Viewer
  open3d draw
  
  # Open a mesh or point cloud file in Open3D Viewer
  open3d draw [filename]
  

  

• [Update] Python examples directory has been refactored for better namespace consistency and new examples have been added.

Neighbor search

• [Update] Updated neighbor search module. The neighbor search module is now equipped with highly optimized built-in implementations of all search methods (Knn, Radius, and Hybrid) supporting both CPU and GPU. Faiss build dependency is removed.

Visualization and GUI

• [New] Introducing raw mode visualization. The raw mode automatically simplifies the lighting environment and object materials to make it easier to inspect the underlying geometry of point clouds and triangle meshes.
• [New] Open3D new visualizer now features CPU Rendering based on Mesa’s LLVMpipe software OpenGL implementation. Interactive applications, demos, and Python scripts are all supported as well as off-screen rendering. This feature can also be used within Docker.
  • Method 1: LD_PRELOAD from the command line

    LD_PRELOAD=/home/open3d/development/mesa-21.3.4/libGL.so python examples/python/visualization/draw.py
    

  • Method 2: Preload library in Python

    import ctypes
    ctypes.cdll.LoadLibrary('/home/open3d/development/mesa-21.3.4/libGL.so')
    import open3d as o3d
    
    mesh = o3d.io.read_triangle_model('/home/open3d/development/FlightHelmet/FlightHelmet.gltf')
    o3d.visualization.draw(mesh)
    

  

• [New] WidgetProxy and WidgetStack widgets allow the creation of user interfaces on the fly (contributed by @forrestjgq).
• [New] Background color option for button widgets (contributed by @forrestjgq).
• [New] Set maximum visible items in a list widget to prevent lists from growing too large (contributed by @forrestjgq).
• [New] Function keys are now bindable (contributed by @forrestjgq).
• [New] Support for specifying intrinsic projection matrix in the new visualizer.
• [New] Add support for scaling 3D labels.
• [Fix] Open3D for TensorBoard plugin does not need Open3D-ML now.
• [Fix] Point picking, text input, and UI layout (contributed by @forrestjgq).

Geometry

• [Fix] Oriented Bounding Box
  • Fixed an issue where the orientation of the OrientedBoundingBox was mirrored.
  • [New] added a new parameter for robust oriented bounding box computation for degenerated point clouds.
• [Fix] Convex hull meshes created from point clouds now have outward-pointing triangles.
• [Update] Added a new parameter for robust convex hull computation.
• [Update] TriangleMesh GetSelfIntersectingTriangles() and related functions like IsWatertight(), GetVolume(), etc. are now more than 4 times faster.
• [Fix] Corrected an issue with io::AddTrianglesByEarClipping() where the algorithm could fail for concave polygons.
• [New] New Python examples for reconstruction and voxelization.
• [Fix] Improved logger performance.

Open3D-ML

• [New] MIT-licensed implementation of RandLANet.
• [New] Intel OpenVINO inference backend (contributed by @dkurt).
• [Fix] Fixed an issue with S3DIS where the loss gets NaN after a few epochs.
• [Fix] Fixed an issue with IoU calculation which fails for large point clouds while running inference in patches.
• [Fix] Fixed an issue where the labels are not correctly ordered in the visualizer.
• [New] Support for Images in Dataset Visualizer (contributed by @ajinkyakhoche).

Acknowledgment

We would like to thank all of our community contributors for their true labor of love for this release!

@ajinkyakhoche @ceroytres @chunibyo-wly @dkurt @forrestjgq @Fuhrmann-sep @jeertmans @junha-l @mag-sruehl @maxim0815 @Nicholas-Mitchell @nigels-com @NobuoTsukamoto @ntw-au @roehling @theNded

Also thanks to the many others who helped the Open3D community by reporting as well as resolving issues.

Open3D 0.14 Release Notes

Summary

We are excited to present the new Open3D version 0.14!

In this release, you will find:

• TensorBoard visualization
• Upgraded GUI API
• New RayCastingScene class
• Improved Tensor performance
• Improved I/O performance
• New 3D learning models in Open3D-ML: Point Transformer and PVCNN
• Improved interoperability with Numpy
• And more

Changes in Installation and Build system

• Open3D now works with Python 3.9. We release Open3D pre-compiled Python packages in Python 3.6, 3.7 3.8, and 3.9.
• Open3D 0.14 is the last version that supports conda installation. Starting from version 0.15, users will need to install Open3D with pip install open3d. We recommend installing Open3D with pip inside a conda virtual environment.
• Git submodules are no longer required in Open3D. You can now clone Open3D with git clone https://github.com/isl-org/Open3D.git without the --recursive flag. Also please note the updated Github URL.
• Open3D will now build in Release mode by default if CMAKE_BUILD_TYPE is not specified. Python is no longer required for building Open3D for C++ users.
• Open3D-ML is now recommended to be used along with PyTorch 1.8.2 and/or Tensorflow 2.5.2. Checkout Open3D-ML for more information.

Tensorboard Integration

Now you can use Open3D within Tensorboard for interactive 3D visualization! At a glance, you can:

• Save and visualize geometry sequences and their properties. This enables interactive visualization and debugging of 3D data and 3DML model training.
• Visualize 3D semantic segmentation and object detection with input data, ground truth, and predictions. In addition, any custom properties for a PointCloud, from scalar to vector, can be easily visualized.
• Synchronize time steps and viewpoints during different runs. This helps debug and monitor the effect of parameter tuning.

Rich PBR materials

Object detection

Semantic segmentation

To get started, write some sample geometry data to a TensorBoard summary with this snippet:

from torch.utils.tensorboard import SummaryWriter  # TensorFlow also works, see docs.
import open3d as o3d
from open3d.visualization.tensorboard_plugin import summary
from open3d.visualization.tensorboard_plugin.util import to_dict_batch
writer = SummaryWriter("demo_logs/")
cube = o3d.geometry.TriangleMesh.create_box(1, 2, 4)
cube.compute_vertex_normals()
colors = [(1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)]
for step in range(3):
    cube.paint_uniform_color(colors[step])
    writer.add_3d('cube', to_dict_batch([cube]), step=step)

Now you can visualize this in TensorBoard with tensorboard --logdir demo_logs. For more details on how to use TensorBoard with Open3D, check out this tutorial.

Visualizer

Further enhancements have been added to the GUI viewer. Now you can:

• Directly visualize tensor-based geometry classes including PointCloud, TriangleMesh, and LineSet.

• Use physically based rendering (PBR) materials that deliver appealing appearance.

• New default lighting environment and skybox improves visual appeal

• Use all the functionality in Tensorboard!

  

import open3d as o3d
import open3d.visualization as vis
a_sphere = o3d.geometry.TriangleMesh.create_sphere(2.5, create_uv_map=True)
a_sphere.compute_vertex_normals()
a_sphere = o3d.t.geometry.TriangleMesh.from_legacy(a_sphere)
# Compare this...
vis.draw(a_sphere)
a_sphere.material = vis.Material('defaultLit')
a_sphere.material.texture_maps['albedo'] =
    o3d.t.io.read_image('examples/test_data/demo_scene_assets/Tiles074_Color.jpg')
a_sphere.material.texture_maps['roughness'] =
    o3d.t.io.read_image('examples/test_data/demo_scene_assets/Tiles074_Roughness.jpg')
a_sphere.material.texture_maps['normal'] =
    o3d.t.io.read_image('examples/test_data/demo_scene_assets/Tiles074_NormalDX.jpg')
# With this!
vis.draw(a_sphere)

A complete, complex demo scene can be found at examples/python/gui/demo-scene.py

Core

• The Open3D Tensor class received a major performance boost with the help of Intel ISPC compiler and optimization for the contiguous code path.

  (See python/benchmarks/core for the benchmark scripts. For each operation, the geometric mean of run times with different data types is reported. The time is measured with an Intel i9-10980XE CPU.)

• A major upgrade of Parallel HashMap is done. Now you can choose from multi-valued HashMap and HashSet depending your value types. A comprehensive tutorial is also available.

• Linear Algebra performance have been optimized for small matrices, especially on 4x4 transformations.

• Semantics for tensor and tensor-based geometry have been improved, especially on device selection.

• Functions expecting a Tensor now accept Numpy arrays and Python lists. For example:

  import open3d as o3d
  import numpy as np
  
  mesh = o3d.t.geometry.TriangleMesh()
  mesh.vertex['positions'] = np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0]], dtype=np.float32)
  mesh.vertex['colors'] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.float32)
  mesh.triangle['indices'] = [[0, 1, 2]]
  o3d.visualization.draw(mesh)
  

I/O

• We now support I/O from/to Numpy compatible .npz .npy formats for Open3D tensors and tensor maps. It is now easier to convert between Open3D geometry classes and Numpy properties.
• We have improved I/O performance for tensor-based point cloud and triangle-mesh file-formats, including .ply, .pcd, .pts. Geometry loading time is hence improved for the stand-alone visualizer app.
• We added support for material data to the MessagePack serialization format of the RPC module.

Geometry

• We introduce a new class RaycastingScene with basic ray intersections functions and distance transform for meshes, utilizing the award-winning Intel Embree library.

  Example code for rendering a depth map:

  import open3d as o3d
  import matplotlib.pyplot as plt
  
  # Create scene and add a cube
  cube = o3d.t.geometry.TriangleMesh.from_legacy(o3d.geometry.TriangleMesh.create_box())
  scene = o3d.t.geometry.RaycastingScene()
  scene.add_triangles(cube)
  
  # Use a helper function to create rays for a pinhole camera.
  rays = scene.create_rays_pinhole(fov_deg=60, center=[0.5,0.5,0.5], eye=[-1,-1,-1], up=[0,0,1],
                                   width_px=320, height_px=240)
  
  # Compute the ray intersections and visualize the hit distance (depth)
  ans = scene.cast_rays(rays)
  plt.imshow(ans['t_hit'].numpy())
  

  Distance transform generated with RaycastingScene:

  

  See the tutorials for more information (Ray casting, Distance queries).

• Normal estimation for tensor PointCloud is supported with the tensor-compatible nearest neighbor search modules.

• Customizable tensor-based TriangleMesh, VoxelBlockGrid, and LineSet are implemented that allow user-defined properties. For example:

  import open3d as o3d
  import open3d.core as o3c
  
  mesh = o3d.t.geometry.TriangleMesh()
  mesh.vertex["positions"] = o3c.Tensor([[0.0, 0.0, 1.0],
                                         [0.0, 1.0, 0.0],
                                         [1.0, 0.0, 0.0],
                                         [1.0, 1.0, 1.0]], dtype=o3c.float32)
  mesh.vertex["my_custom_labels"] = o3c.Tensor([0, 1, 2, 4], dtype=o3c.int32)
  mesh.triangle["indices"] = o3c.Tensor([[0, 1, 2], 
                                         [1, 2, 3]], dtype=o3c.int32)
  

Pipelines

• We have enhanced point cloud registration (ICP) with a tensor interface:
  • Float64 (double) precision point cloud is supported for a higher numerical stability
  • Robust Kernels, including Huber, Tukey, and GM losses are supported for robust registration.
  • Colored-ICP is now supported in the unified tensor-based API.
  • See this tutorial for more details.
• We also provide an initial tensor-based reconstruction system in Python, including
  • Customizable volumetric RGB-D integration;
  • Dense RGB-D SLAM with a GUI;
  • See this tutorial for more details.

Open3D-ML

The Open3D-ML library welcomes more state-of-the-art models and operators that are ready to use for advanced 3D perception, especially semantic segmentation, including

• New state-of-the-art Point Transformer for Semantic Segmentation.
• Highly Efficient Point-Voxel Convolution for Semantic Segmentation.
• RaggedTensor integration that enables batch SparseConvolution and SparseConvolutionTranspose along with PyTorch.
• Batched voxelization for fast point-voxel conversions.

Refer to the tutorial for training and inference on new models. (PyTorch TensorFlow).

Expreimental ARM64 builds

• Experimental Ubuntu ARM64 build
  • Python 3.6: open3d-0.14.1-cp36-cp36m-linux_aarch64.whl
  • Python 3.7: open3d-0.14.1-cp37-cp37m-linux_aarch64.whl
  • Python 3.8: open3d-0.14.1-cp38-cp38-linux_aarch64.whl
  • Python 3.9: open3d-0.14.1-cp39-cp39-linux_aarch64.whl
  • Visualizer does not work on Raspberry Pi due to the lack of full OpenGL
  • Tested on Nvidia Jetson boards with Ubuntu 18.04
  • You may use pyenv to setup your virtualenvs
• Experimental Apple Silicon M1 ARM64 build:
  • Python 3.8: open3d-0.14.1-cp38-cp38-macosx_11_0_arm64.whl
  • Python 3.9: open3d-0.14.1-cp39-cp39-macosx_11_0_arm64.whl
  • Tested on M1 macOS Monterey
  • You may use miniconda to setup your virtualenvs
• Test your installation

  pip install open3d-xxx.whl
  # Test the new vidualizer
  python -c "import open3d as o3d; c = o3d.geometry.TriangleMesh.create_box(); o3d.visualization.draw([c])"
  # Test the traditional visualizer
  python -c "import open3d as o3d; c = o3d.geometry.TriangleMesh.create_box(); o3d.visualization.draw_geometries([c])"
  

Acknowledgment

We thank all the community contributors for this release! (alphabetical order) @cclauss @chrockey @chunibyo-wly @cosama @forrestjgq @gsakkis @junha-l @ktsujister @leomariga @li6in9muyou @marcov868 @michaelbeale-IL @muskie82 @nachovizzo @NobuoTsukamoto @plusk01 @ShreyanshDarshan @ShubhamAgarwal12 @SoftwareApe @stanleyshly @stotko @theNded @zhengminxu

Open3D 0.13.0 Release Notes

We welcome you to the 0.13.0 release of Open3D. This release is full of exciting new features with a strong emphasis in real-time pipelines, but also full of bug fixes and usability improvements. The big highlights of this release are as follows:

Click in the image above to watch the presentation video, or visit:

https://www.youtube.com/watch?v=pLCVCH7ypI4

• New real-time 3D reconstruction pipeline, featuring GPU and CPU support based on VoxelHashing.
• New real-time point cloud registration algorithm, featuring a high-performance version of Iterative Closest Point (ICP).
• New Neighbor Search module, introducing your favorite search algorithms such as KNN and RadiusSearch, with support for GPU and CPU devices through a common interface.
• New web visualizer, which enables users to access the advanced rendering and visualization features of Open3D in your favourite web environments (remote and locally!), including Jupyter notebooks, Jupyter lab, and standalone web applications.
• New 3D machine learning models and datasets, featuring PointRCNN for 3D object detection, SparseConvNets for point cloud semantic segmentation, and support for ScanNet and SunRGBD.
• Upgraded GUI module, providing improved and more versatile versions of existing widgets, and new ones: ImageWidget and ToggleSwitch.
• Upgraded build system, adding support for CUDA 11.

Real-time 3D reconstruction

We introduce a new CUDA accelerated pipeline including RGBD odometry, frame-to-model tracking, and volumetric integration.

Figure 1. Example of 3D reconstruction from an RGB-D sensor.

Odometry

We introduce the tensor based real-time RGBD Odometry pipeline. In addition to the legacy Hybrid and Intensity based methods, we support the popular point-to-plane method.

TSDFVoxelGrid

We further accelerate volumetric integration and introduce fast ray casting for rendering.

VoxelHashing

Based on the accelerated RGBD odometry and raycasting, we present the fully functional VoxelHashing system. It performs dense volumetric reconstruction with fast frame-to-model tracking. We present an easy-to-use GUI that also shows real-time interactable surface reconstruction.

SLAC

We have further enhanced our legacy offline reconstruction system by introducing the Simultaneous Localization and Calibration (SLAC) algorithm. This algorithm applies advanced dense multi-way registration along with non-rigid deformation to create highly-accurate reconstructions.

Real-time point cloud registration

We present a high-performance implementation of ICP using Open3D’ Tensor library. This module is one of the first on leveraging the new Neighbor search module and the newly crafted parallel kernels. This implementation brings support for multi-scale ICP, which allows us to do iterations on different resolutions in order to accelerate convergence while keeping computation low.

Figure 2. ICP registration of multiple point clouds from a driving dataset.

New Neighbor Search module

Neighbor search is at the core of many 3D algorithms. Therefore, it is critical to have access to a fast implementation able to execute a large number of queries in a fraction of a second. After months of development, the Open3D team is proud to present the new Neighbor Search module!

This module brings support for core search algorithms, such as KNN, Radius search, and Hybrid search. All these algorithms are provided with support for both CPU and GPU, through a common and easy-to-use interface. Write your code once and support multiple devices! Moreover, we have not sacrificed a single flop of computation, making this module one of the fastest neighbor search libraries ever created.

Web visualizer

The need for visualizing complex 3D data in web environments has surged considerably in the past few years, in part thanks to the proliferation of sensors like LIDAR and RGBD cameras. New use cases, such as online dataset inspection and remote visualization are now an integral part of many tasks, requiring the crafting of ad-hoc tools, which often are cumbersome to use.

Figure 3. Standalone visualization of a semantic segmentation model in a browser.

In order to improve this situation, we introduce our new web-based visualization module, which enables 3D visualization from any browsers and any location. This module lets users run advanced rendering and visualization pipelines, both remote and locally through your web browser. All the power of Open3D’ rendering engine --including support for PBR materials, multiple lighting systems, 3D ML visualization, and many other features--, are now supported in your browser. This module also includes a Jupyter extension for interactive web-based visualization! This new feature allows you to run compute-intensive 3D processing in a dedicated server while visualizing the results remotely on any device through your browser.

Figure 4. Visualization of a 3D model on a Jupyter notebook.

3D Machine Learning

Architectures

In this release, we introduce a new point cloud semantic segmentation architecture based on a Sparse Convolution-based UNet model. This architecture leverages the new sparse convolution operators provided by Open3D, and achieves state of art performance for Semantic Segmentation on the ScanNet dataset. We have also added support for PointRCNN for the task of 3D object detection. To enable PointRCNN we have added new operators like furthest_point_sampling, three_interpolate, ball_query, which are available through Open3D for TensorFlow and Pytorch.

Figure 5. Example of 3D semantic segmentation using a SparseConvUNet model on ScanNet.

Figure 6. Example of 3D object detection using a PointRCNN on KITTI.

All these models are provided with their respective training and inference pipelines with support for TensorFlow and Pytorch. Pre-trained models are also provided (check out the following link).

Datasets

This release brings the following datasets: Scannet and SunRGBD.

Frameworks

We now support all models on newer versions of TensorFlow (2.4.1) and PyTorch (1.7.1), on CUDA 11.0.

GUI and rendering modules

Open3D 0.13.0 brings a cascade of improvements and fixes to the renderer and GUI modules.

• The camera can now be controlled with respect to a target object, and the pan-in/pan-out actions are smoother.
• The render supports render targets.
• The black screen issue in MacOS systems is now solved.
• Support for a video widget
• The background of the 3D scene widget can be updated with an image.
• Camera objects can be visualized in the 3D scene.
• Camera objects support intrinsic and extrinsic parameters.
• Support for multiple fonts.
• Support for a new ToggleSwitch widget.
• New GUI/rendering examples for common tasks, such as data acquisition from RGBD sensors, 3D reconstruction, and registration, among others.

Build System

Our pip packages now include support for CUDA 11.0, PyTorch 1.7.1, and TensorFlow 2.4.1 to enable RTX 3000 series devices. Please, notice that we provide custom PyTorch wheels for Linux to work around an incompatibility between CUDA 11, PyTorch, and extension modules such as Open3D-ML.

This release also brings new improved support for CUDA on Windows. Users can now build CUDA accelerated Python wheels for Windows. Open3D is now built with security options enabled by default.

We hope you find Open3D 0.13.0 exciting and useful. Happy coding!

Remember that you can reach out with questions, requests, or feedback through the following channels:

• e-mail
• Forum
• Discord network

Find the full change log here.

The Open3D team

Open3D 0.12.0 Release Notes

Open3D 0.12.0 is out, and it comes with new 3D object detection pipelines and datasets, the newest versions of some of your preferred classic tools, and many bug fixes. Check out our 0.12.0 release video:

Extensions to the Open3D-ML module

The previous release of Open3D introduced an exciting new module dedicated to 3D Machine Learning Open3D-ML, featuring support for 3D semantic segmentation workflows. In this release, we have extended Open3D-ML with the task of 3D object detection. This extension introduces support for new datasets, such as the Waymo Open dataset, Lyft level 5 open data, Argoverse, nuScenes, and KITTI. As always, all these datasets can be visualized out-of-the-box using our visualization tool, from Python or C++. The visualization tool is now equipped with the capability to render 3D bounding boxes along with all the previously existing modalities, e.g. semantic labels, XYZRGB, depth, normals, etc.

Open3D-ML features

PointPillars, the first of the many object detection models to come in the near future. To enable the implementation of PointPillars, we have added a set of new ML operators in Open3D, such as: grid_sampling, NMS, and IOU. These operators are available to the community and can be used to build new models, using our Python and C++ APIs.

import os
import open3d.ml as _ml3d
import open3d.ml.torch as ml3d

cfg_file = "ml3d/configs/pointpillars_kitti.yml"
cfg = _ml3d.utils.Config.load_from_file(cfg_file)

model = ml3d.models.PointPillars(**cfg.model)
cfg.dataset['dataset_path'] = "/path/to/your/dataset"
dataset = ml3d.datasets.KITTI(cfg.dataset.pop('dataset_path', None), **cfg.dataset)
pipeline = ml3d.pipelines.ObjectDetection(model, dataset=dataset, device="gpu", **cfg.pipeline)

... 
# run inference on a single example.
result = pipeline.run_inference(data)

We have also updated our model zoo, providing new pretrained models on KITTI for the task of 3D object detection, and new semantic segmentation models on Paris-Lille3D and Semantic3D.

Remember that all the tools provided in Open3D-ML are compatible with PyTorch and TensorFlow!

Support for RealSense SDK v2

RealSense sensors’ support has been upgraded to leverage the RealSense SDK v2. Users can now capture crisp 3D data from L515 devices. As part of this upgrade, we include support for Bag files format (RSBagReader), and direct streaming from sensors. These operations can now be done through a new sensor class: RealSenseSensor, offering a simple and intuitive way to control your sensors.

import open3d as o3d
bag_reader = o3d.t.io.RSBagReader()
bag_reader.open(bag_filename)
while not bag_reader.is_eof():
    im_rgbd = bag_reader.next_frame()
    # process im_rgbd.depth and im_rgbd.color

bag_reader.close()

import json
import open3d as o3d
with open(config_filename) as cf:
    rs_cfg = o3d.t.io.RealSenseSensorConfig(json.load(cf))

rs = o3d.t.io.RealSenseSensor()
rs.init_sensor(rs_cfg, 0, bag_filename)
rs.start_capture(True)  # true: start recording with capture
for fid in range(150):
    im_rgbd = rs.capture_frame(True, True)  # wait for frames and align them
    # process im_rgbd.depth and im_rgbd.color

rs.stop_capture()

For further information, check this tutorial.

CORE and 3D reconstruction

Open3D 0.12 brings exciting CORE upgrades, including a new Neighbor Search module. This module supports typical neighbor search methods, such as KNN, radius search, and hybrid search, on both CPUs and GPUs, under a common interface!

Furthermore, we have created a new version of the TSDF integration algorithm accelerated on GPU. This version is able to achieve outstanding computational performance, requiring between 2 and 4 ms to integrate a pair of frames.

New rendering functionalities

We have done an important effort over the last months to put out a modern, real-time, rendering API. This effort is still ongoing, and we are committed to bringing top-tier rendering capabilities with a strong emphasis in performance, versatility, ease of use, and beauty. As part of our commitment, in this release we have added relevant extensions to this API:

• Support for Screen-space reflections

• Full programmatic support for headless rendering in Filament (for real)

box = o3d.geometry.TriangleMesh.create_box(2, 2, 1)
render = rendering.OffscreenRenderer(640, 480)
render.scene.add_geometry("box", box, grey)
render.scene.camera.look_at([0, 0, 0], [0, 10, 0], [0, 0, 1])
img = render.render_to_image()

• Support for arbitrary camera intrinsic matrices: A small step for the Camera class; a very anticipated step by the Open3D community

Camera::SetProjection(const Eigen::Matrix3d& intrinsics,
                               double near,
                               double far,
                               double width,
                               double height)

• Support for text rendering: Render text in 3D

Label3D::Label3D(const Eigen::Vector3f& pos, const char* text)

• Full control over the color grading pipeline

class ColorGradingParams {
public:
    ColorGradingParams(Quality q, ToneMapping algorithm);

    void SetTemperature(float temperature);
    void SetTint(float tint);
    void SetContrast(float contrast);
    void SetVibrance(float vibrance);
    void SetSaturation(float saturation);
    void SetChannelMixer(const Eigen::Vector3f& red,
                         const Eigen::Vector3f& green,
                         const Eigen::Vector3f& blue);
    void SetShadowMidtoneHighlights(const Eigen::Vector4f& shadows,
                                    const Eigen::Vector4f& midtones,
                                    const Eigen::Vector4f& highlights,
                                    const Eigen::Vector4f& ranges);
    void SetSlopeOffsetPower(const Eigen::Vector3f& slope,
                             const Eigen::Vector3f& offset,
                             const Eigen::Vector3f& power);

    void SetCurves(const Eigen::Vector3f& shadow_gamma,
                   const Eigen::Vector3f& midpoint,
                   const Eigen::Vector3f& highlight_scale);
}

Control shadow behaviors and post-processing effects:

class View
{
    void SetPostProcessing(bool enabled);
    void SetAmbientOcclusion(bool enabled, bool ssct_enabled);
    void SetAntiAliasing(bool enabled, bool temporal);
    void SetShadowing(bool enabled, ShadowType type);
}

Visualization and GUI: O3DViewer (beta)

The visualization module has been extended, using the new rendering capabilities and the GUI API, to create a unified visualizer displaying all the features contained in previous Open3D visualizers, e.g., camera animation, data selection, support for callbacks, and multiple shading modes.

This new visualizer, codename O3DViewer, will be the official visualization tool in Open3D starting in Open3D 0.14. At that time, previous visualizers will be deprecated.

We hope you find Open3D 0.12.0 exciting and useful. Happy coding!

Remember that you can reach out with questions, requests, or feedback through the following channels:

• e-mail
• Discourse forum
• Discord network

Find the full changelog here.

The Open3D team

We are excited to present Open3D 0.11.0!

Open3D 0.11.0 introduces a brand new 3D Machine Learning module, nicknamed Open3D-ML. Open3D-ML is an extension of your favorite library to bring support for 3D domain-specific operators, models, algorithms, and datasets. In a nutshell, users can now create new applications combining the power of 3D data and state-of-the-art neural networks! Open3D-ML is included in all the binary releases of Open3D 0.11.0.

Open3D-ML comes with support for Pytorch +1.4 and TensorFlow +2.2, the two most popular machine learning frameworks. The first iteration of this module features a 3D semantic segmentation toolset, including training and inference capabilities for RandlaNet and KPConv. The toolset supports popular datasets such as SemanticKITTI, Semantic3D, 3D Semantic Parsing of Large-Scale Indoor Spaces S3DIS, Toronto3D, and Paris-Lille-3D. Open3D-ML also provides a new model zoo compatible with Pytorch and TensorFlow, so that users can enjoy state-of-the-art semantic segmentation models without hassles.

We have endowed the new Open3D-ML module with a new data viewer tool. Users can now inspect their datasets and model’s predictions in an intuitive and simple way. This visualization tool includes support for Pytorch and TensorFlow frameworks and is fully customizable due to its Pythonic nature.

This viewer has been built upon the new visualization API, integrating the new Rendering and GUI modules. Thanks to the new visualization API, users can perform advanced rendering, fully programmatically from Python and C++. Users can also create slick GUIs with a few lines of Python code. Check out how to do this here.

The Open3D app has also been extended to include the following features:

• Support for FBX and glTF2 assets
• Full support for PBR models.

Open3D 0.11 includes for the first time support for Linux ARM (64-bit) platforms. This has been a long-time requested feature that finally made it into the release. You can now enjoy all Open3D features, including our new rendering and visualization pipelines in OpenGL-enabled ARM platform.

[Breaking] Please, notice that the API and the structure of Open3D have changed considerably after an intense refactoring process. You will need to update your code to use the new namespaces. Please, check the full changelog and the documentation for further information.

We hope you find Open3D 0.11.0 exciting and useful. Happy coding!

Remember that you can reach out with questions, requests, or feedback through the following channels:

• e-mail
• Discourse forum
• Discord network

Find the full changelog here.

The Open3D team

We are proud to present the 0.10.0 release of Open3D!

For this release, the Open3D team set its focus on the theme of Visualization and Rendering. For starters, we upgraded Open3D rendering capabilities, adding a new real-time renderer based on Filament. This renderer brings support for spatially-varying BRDFs, the Cook-Torrance model, Image-Based Lighting, and Physically-based rendering, among many other improvements. Overall, this translates into a much better rendering quality, endowing 3D models of a higher realism and beauty.

[Warning] As a consequence, we are deprecating the traditional rendering system in favor of the new one. But do not panic, the new rendering system and the traditional system will live together until the 0.12.0 release of Open3D when it will be officially removed from the project.

In order to improve the process of 3D visualization, Open3D has incorporated a new GUI module. It was decided to base the new GUI module on the successful Dear ImGui project due to its compact size and the possibilities of its immediate mode. This new GUI module will help 3D developers to build tailored nice-looking graphical applications with minimum effort.

As an example of what can be done with the GUI module, the Open3D team has developed a new standalone application for 3D visualization, in combination with the new rendering engine. The Open3D 3D visualizer is the quickest and easiest way of making your models look outstanding! Try it yourself by downloading here (MacOS).

It is believed that one of the most critical aspects of an open-source project is the quality of its documentation. For this reason, in every release, the team makes a big effort to bring you documentation of the highest quality possible. On this occasion, we decided to upgrade our tutorials to make them interactive. This allows users to directly play and experiment with the concepts presented in each tutorial. The new tutorials, in Jupyter notebook format, can be found here.

Finally, the GPU support that many of you have been requesting has started to make its way into master. In this first step, Open3D brings experimental support for a new Tensor library, that can be transparently used in CPUs and GPUs. We will keep migrating the entire library step-by-step, so please be patient. Feel free to test it out and provide us with feedback.

We hope you find Open3D 0.10.0 useful. Happy coding!

Remember that you can reach out with questions, requests, or feedback through the following channels:

• e-mail
• Discourse forum
• Discord network

Find the full changelog here.

The Open3D team

Open3D 0.9.0 Release Notes

The Open3D team and the Open Source Vision Foundation are proud to present the 0.9.0 release of the Open3D library.

This release brings two exciting algorithms for processing point clouds and meshes: Poisson surface reconstruction and as-rigid-as-possible deformation. You can find a reference for how to use these algorithms in the following examples:

• surface_reconstruction_poisson.py

• mesh_deformation.py

The problems related to headless mode rendering have also been solved in this release, among other bug fixes!

This is the last release of Open3D with support for Python 2.7.

On behalf of the Open3D team, we would like to thank our technical writer, Rohan Rathi and the Google season of docs organization for their help in the critical task of improving Open3D documentation. Your contribution has served to make Open3D more accessible to new users.

We are also happy to announce our new Discourse forum, which becomes the official way to discuss issues related to Open3D.

In an effort to improve our communication with the Open3D community and all the developers that use this library, we have created a new mailing list: [email protected]. Here developers and contributors can obtain first-hand information about the development progress of Open3D, the library’s roadmap, and information on how to proceed to implement new features. You can also join the mailing list via Google groups

Take a look at our documentation Open3D docs to see all the details, and send us feedback at [email protected]. You can also join our Discourse forum or the Discord network to participate in the development discussions.

Find the full changelog here. Enjoy!

The Open3D team

The Open3D team and the Open Source Vision Foundation are excited to share with the community Open3D 0.8.0!

Starting from this release, we'll be using the open3d package name instead of open3d-python on PyPI. You can now install Open3D with

# Pip
pip install open3d

# Conda
conda install -c open3d-admin open3d

In this release, we bring support for the brand new Azure Kinect sensor. Users can enjoy the benefits of this RGB-D sensor through the simple Python and C++ APIs provided by Open3D.

We bring in a new geometry type, the Tetrahedral Mesh, which supports Delaunay triangulation from PointCloud, isosurface extraction toTriangleMesh and visualization. We also bring in two algorithm implementations, check out Ball Pivoting and DBSCAN.

As usual, we received many contributions from the community. Thank you all for your contribution and support! We also worked extensively on fixing issues brought up by the community. Please, have a look at our documentation Open3D docs to see all the details, and send us feedback at [email protected]. You can also join our Discord network to participate in the development discussions.

To see the full list of contributions and the release video, please check our blog post. Enjoy! The Open3D team

Open3D 0.7.0 Release Notes

The Open3D team and the Open Source Vision Foundation are proud to present the 0.7.0 release of the Open3D library.

This release is focused on extending the functionality of Open3D data types such as Octree, VoxelGrid, and Mesh. Among the main novelties we would like to highlight:

• New generic Octree data type with arbitrary leaves
• Upgraded VoxelGrid data type
• Conversion functionalities from Point Cloud to VoxelGrid and Octree
• Upgraded Mesh data type
• Visualization support for Octree and VoxelGrid
• New sampling methods for TriangleMesh
• New Mesh filter functionalities: Sharpen, Smooth, Taubin
• Upgraded TSDFVolume generation

We also tackled a set of issues brought up by the community, including support of finer control over the geometry of the Visualizer. Now it is possible to add and remove geometries dynamically! Please, have a look at our documentation (Open3D docs) to see all the details, and send us feedback at [email protected]. You can also join our Discord network to participate in the development discussions.

Find the full change log here. Enjoy! The Open3D team

The Open3D team and the Open Source Vision Foundation are excited to announce the 0.6.0 release of the Open3D library. In this release, we focused our efforts on improving the quality of Open3D documentation and paving the way for upcoming GPU support.

Documentation is a critical aspect of any software project, but it becomes especially critical in open-source projects. This is one of the main ways we engage with the community. For this reason, we improved the internal infrastructure to automatically generate documentation in a new format, which makes the Python API more readable and easy to understand. Please take a look at Open3D docs.

The team has also been working on bringing multi-GPU support to Open3D. We will start rolling this out in upcoming releases. In the meantime, feedback and suggestions are welcomed. Please check our GPU integration branch here.

This release also includes new data types that serve as the foundation for new meshing algorithms that will be rolled out in our next release.

Find the full change log here.

Please send us feedback at [email protected] and join our Discord network to participate in the discussions.

Enjoy! The Open3D team

Open3D 0.5.0 Release Notes

The Open3D team and the Open Source Vision Foundation (http://www.osvf.org) are excited to announce the 0.5.0 release of the Open3D library.

In this release we show the power of Open3D as a core tool to create machine learning solutions for 3D data.

We introduce a re-implementation of the PointNet++ architecture to perform point cloud semantic segmentation using Open3D and TensorFlow. Our Open3D-PointNet++ is able to produce highly accurate results in the Semantic3D benchmark, surpassing the results of the original PointNet++ implementation. Even more exciting is the fact that our re-designed Open3D-PointNet++ is able to perform real-time inference (+10 FPS) on the KITTI dataset. We show how to perform training and inference of Open3D-PointNet++ in both Semantic3D and KITTI. Check out this blog post for more information!

We have also added a new VoxelGrid representation and tooling to convert from point clouds to a VoxelGrid structure. This functionality is extremely useful to produce representations that are easier to digest by neural networks.

We have also done significant improvements to our internal infrastructure, including a simplified CI testing mechanism via docker images, enhanced testing coverage, and easier installation of the library.

Find the full change log here.

Enjoy! The Open3D team

Open3D Version 0.4 (released 2018-10-25)

The Open3D team and the Open Source Vision Foundation (http://www.osvf.org) are proud to announce the release of the 0.4 version of the Open3D library.

This release brings support for RealSense RGB-D sensors to Open3D, enabling functionalities such as real-time RGB-D capturing and a new point cloud viewer. We have also added new documentation and examples using RealSense sensors to create 3D reconstructions.

We are also excited to introduce support for Jupyter notebooks with a brand new WebGL widget to perform advanced 3D visualization from the comfort of your browser.

One of our main goals is to leverage Open3D as a tool to simplify the use of state-of-the-art 3D pipelines like those used in computer vision and machine learning. With this goal in mind, we are proud to introduce the Open3D Ecosystem, a set of repositories that make use of Open3D to create powerful applications. The first member of this ecosystem is Open3D-PointNet [link], a version of the famous machine learning architecture [link] for point cloud classification and semantic segmentation, which is now fully usable from the commodity of Open3D routines.

From a project infrastructure perspective, we just finalized the integration with TravisCI to perform automatic unit testing. This is a large step forward in terms of quality control for Open3D and it will help to make our software more reliable while expediting its development. The team has made an enormous effort to define UnitTests for all the functionalities present in the library, a task that will be concluded in future releases.

Check out our release video [link] to see these functionalities in action!

For a detailed description of all the features of Open3D 0.4, please keep reading. We hope you enjoy this release and hope to hear from you. Please send us feedback at [email protected] and join our Discord network to participate in the discussions.

Thanks! The Open3D team

Open3D Version 0.3 (2018-09-11)

Open3D is being developed under the auspices of the Open Source Vision Foundation (http://www.osvf.org). The team has been working hard to make Open3D accessible and easy to use.

In this regard, version 0.3 brings major features related to library installation, including improved CMake installation for Linux, Mac and Windows with off-the-shelf systems; new installation options using PIP and CONDA for Linux, Mac and Windows; and overall an easier and cleaner installation experience.

We are also continuing to extend the 3D processing and visualization functionality. Among other features, version 0.3 brings support for enhanced 3D reconstruction; extension of TSDF volume integration to floating-point intensity images; and an improved non-blocking visualization tool.

This version also comes with extended and improved documentation. We have enhanced the tutorials on multiway registration, marching cubes, global registration, and headless rendering, among others.

Open3D 0.3 also includes our first set of tests to verify the integrity and correctness of the library. You can expect to see much more of this in future releases.

For a detailed description of all the features of Open3D 0.3, please read our blog post! . We hope you enjoy this release and hope to hear from you. Please send us feedback at [email protected] and join our Discord network (https://discord.gg/D35BGvn) to participate in the discussions.

Thanks! The Open3D team

Key updates:

• Headless rendering
• Non-blocking visualization
• Docker for Open3D
• Refined build system
• Fast global registration
• Color map optimization
• PyPi support
• Changing Python package name: py3d -> open3d
• Many bug fixes

Minor fixes on

• reading rgb channels from colored point cloud files written in ascii
• pybind cmake_list

Misc

• polishing documents
• adding a script for installing necessary python modules

First Open3D release with following core features

• Basic 3D data structures
• Basic 3D data processing algorithms
• Scene reconstruction
• Surface alignment
• 3D visualization
• Python binding