Open3D release 0.2 is here!

Version 0.2 (Release date July 1st, 2018)

The first major update of Open3D, with various additional features and bug fixes.


  1. Headless rendering: GLFW 3.3 dev + OSMesa combinations for supporting headless rendering. This feature is especially useful for users who want to get depth/normal/images from a remote server without physical monitors.
  2. Non-blocking visualization: draw_geometries() is a useful function for quick overview of static geometries. However, this function holds process until a visualization window is closed. Non-blocking visualization shows a live update of geometry while the window is open.
  3. Mesh cropping: In v0.1 VisualizerWithEditing only supports point cloud cropping. The new version supports mesh cropping as well.
  4. Point cloud picker with an application of manual point cloud registration

Docker for Open3D: a new Docker CE based solution for utilizing Open3D. With this update, you can:

  1. sandbox Open3D from other applications on a machine.
  2. operate Open3D on a headless machine using VNC or the terminal.
  3. edit the Open3D code on the host side but run it inside an Open3D container.

Additional features:

  1. Fast global registration: Open3D’s implementation of ‘fast global registration’ paper [Zhou et al 2016]. For the task of global registration, the single threaded fast global registration is about 20 times faster than RANSAC based implementation.
  2. Color map optimization: Another interesting application for copying seamless texture map to the reconstructed geometry. This is an implementation of ‘’Color Map Optimization for 3D Reconstruction with Consumer Depth Cameras” paper [Zhou and Koltun 2014].  The optimization pipeline creates sharp texture mapping on the geometry taken with color cameras.
  3. Basic operations for color and depth images: new function that can generate a depth discontinuity mask from a depth image. New dilation operators for making a thicker discontinuity mask.

Enhancement on build system:

  1. Refined CMake build system: polished CMake build system so that it can fully support make install or make uninstall. Once installed, Open3D library is searchable using find_package module in CMake. CMakeList.txt file for the new application is simplified.
  2. PyPi support: Newer version provides pip install which is a more convenient way to begin using Open3D. Try ‘pip install open3d-python’ and ‘import Open3D’ in Python.
  3. Basic test framework: Adding initial support for ‘Gtest’ examples to test Open3D’s functions and classes.


  1. Changing Python package name: python package name is changed from py3d to open3d.
  2. Many bug fixes:
    1. Bug fix on the Reconstruction system regarding defining and utilizing information matrix.
    2. Additional materials on Open3D documents.

Open3D release 0.1

Version 0.1 (Release date Feb 22nd, 2018)

First official release. Open3D is an open-source library that supports rapid development of software that deals with 3D data. Open3D has the following core features:

Core principle

  1. Started from scratch.
  2. Focus on implementation of basic widely-used 3D processing algorithms.
  3. Does not depend on heavyweight libraries.
  4. Minimum lines of code
  5. The library can be built and run from source using Ubuntu/MacOSX/Windows systems.

Basic 3D data structures

  1. Open3D provides point cloud, triangle mesh, image, and pose graph data structures.
  2. Each data structure has its own I/O interface with various files.
  3. Supported file formats:
    1. Image: JPEG, PNG.
    2. 3D geometry: Bin, PCD, PLY, PTS, XYZ, XYZN, XYZRGB.
    3. camera trajectory & pose graph: JSON, LOG.

Basic data processing algorithms

  1. Point cloud downsampling, normal estimation, and vertex coloring.
  2. Gaussian and Sobel filter for image processing.
  3. Scalable TSDF volume integration.

Scene reconstruction

  1. Provides basic scene reconstruction system specialized for RGBD sequence.
  2. The system consists of RGBD Odometry, pose graph optimization, and TSDF volume integration.
  3. The integration volume can be scalable.

Surface alignment

  1. Implementation of local geometric feature: ‘Fast Point Feature Histograms (FPFH)’ paper [Rusu and Beetz 2009].
  2. Point-to-point/point-to-plane/colored ICP implementations with OpenMP acceleration.
  3. Provides a basic, RANSAC-based global registration pipeline.
  4. Fast pose graph optimization: implementation of ‘Robust reconstruction of indoor scenes’ paper[Choi et al 2015].
  5. In house convex optimization: Gauss-Netwon and Levenberg-Marquardt methods.

3D visualization

  1. Quick overview of point cloud, mesh, image.
  2. Various options to customize camera path or camera intrinsics.
  3. Color/depth/normal rendering supported.
  4. Providing rendering buffer access to save rendered images.
  5. Can configure custom key callback functions.
  6. Can select a region and crop of crop point cloud.

Python binding

  1. The c++ functions/classes/definitions are exposed to the Python API.
  2. The Python API provides a quick debug cycle for development of novel algorithms.