CPU (Software) Rendering

Open3D’s new visualization functionality (O3DVisualizer class, draw() function and open3d.visualization.gui and open3d.visualization.rendering modules) requires a recent GPU with support for OpenGL 4.1 or higher. This is not available in certain situations:

• GPU is too old to support OpenGL 4.1.

• No GPU is available, for example on cloud servers that do not have any GPU (integrated or discrete) installed, or in a docker container that does not have access to the host GPU. This is often the case for many cloud based Jupyter notebooks such as Google Colab, Kaggle, etc.

• A GPU is available, but it only supports computation, not graphics. This is a common scenario for cloud based Jupyter notebooks deployed in docker containers.

Open3D supports CPU or software rendering in such situations. Note that this usually produces slower and less responsive rendering, so a GPU is recommended. Currently, this is available only for Linux. There are two separate ways to use CPU rendering depending on whether interactive or headless rendering is desired. Both methods are described below.

Headless CPU Rendering

For Python code, you can enable CPU rendering for headless rendering when using the :class: .OffscreenRenderer for a process by setting an environment variable before importing Open3D:

- ``EGL_PLATFORM=surfaceless`` for Ubuntu 20.04+ (Mesa v20.2 or newer)
- ``OPEN3D_CPU_RENDERING=true`` for Ubuntu 18.04 (Mesa older than v20.2).

Here are the different ways to do that:

# from the command line (Ubuntu 20.04+)
EGL_PLATFORM=surfaceless python examples/python/visualization/render_to_image.py
# or Ubuntu 18.04
OPEN3D_CPU_RENDERING=true python examples/python/visualization/render_to_image.py

# In Python code
import os
os.environ['EGL_PLATFORM'] = 'surfaceless'   # Ubunu 20.04+
os.environ['OPEN3D_CPU_RENDERING'] = 'true'  # Ubuntu 18.04
import open3d as o3d

# In a Jupyter notebook
%env EGL_PLATFORM surfaceless   # Ubuntu 20.04+
%env OPEN3D_CPU_RENDERING true  # Ubuntu 18.04
import open3d as o3d

Note

Setting the environment variable after importing open3d will not work, even if open3d is re-imported. In this case, if no usable GPU is present, the Python interpreter or Jupyter kernel will crash when visualization functions are used.

Note

This method will not work for interactive rendering scripts such as examples/python/visualization/draw.py. For interactive rendering see the next section.

Interactive CPU Rendering

The method for enabling interactive CPU rendering depends on your system:

1. You use Mesa drivers v20.2 or higher. This is the case for all Intel GPUs and some AMD and Nvidia GPUs. You should be running a recent Linux OS, such as Ubuntu 20.04. Check your Mesa version from your package manager (e.g. run dpkg -s libglx-mesa0 | grep Version in Debian or Ubuntu). In this case, you can switch to CPU rendering by simply setting an environment variable before starting your application. For example, start the Open3D visualizer app in CPU rendering mode with:

   LIBGL_ALWAYS_SOFTWARE=true Open3D
   

   Or for Python code:

   LIBGL_ALWAYS_SOFTWARE=true python examples/python/visualization/draw.py
   

Note

Mesa drivers must be in use for this method to work; just having them installed is not sufficient. You can check the drivers in use with the glxinfo command.

2. You use Nvidia or AMD drivers or old Mesa drivers (< v20.2). We provide the Mesa software rendering library binary for download here. This is automatically downloaded to build/_deps/download_mesa_libgl-src/libGL.so.1.5.0 when you build Open3D from source. The prebuilt version works on Ubuntu 18.04 and Ubuntu 20.04. If you want to use CPU rendering all the time, install this library to /usr/local/lib or $HOME/.local/lib and prepend it to your LD_LIBRARY_PATH:

   export LD_LIBRARY_PATH=$HOME/.local/lib:$LD_LIBRARY_PATH
   

   For occasional use, you can instead launch a program with CPU rendering with:

   LD_PRELOAD=$HOME/.local/lib/libGL.so.1.5.0 Open3D
   

   Or with Python code:

   LD_PRELOAD=$HOME/.local/lib/libGL.so.1.5.0 python
   examples/python/visualization/draw.py