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https://github.com/um-arm-lab/sdf_tools

Builds 2D signed distance fields from images, 3D signed distance fields from pointclouds, 3D signed distance fields from Octomap, provides a lightweight signed distance field library, message types for signed distance fields, and tools to compress signed distance fields for transport.
https://github.com/um-arm-lab/sdf_tools

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Builds 2D signed distance fields from images, 3D signed distance fields from pointclouds, 3D signed distance fields from Octomap, provides a lightweight signed distance field library, message types for signed distance fields, and tools to compress signed distance fields for transport.

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README

        

Builds 2D signed distance fields from images, 3D signed distance fields from pointclouds, 3D signed distance fields from Octomap, provides a lightweight signed distance field library, message types for signed distance fields, and tools to compress signed distance fields for transport.

A simple example/tutorial is provided, see the [Wiki](https://github.com/UM-ARM-Lab/sdf_tools/wiki).

# Python Bindings

These will build by default, and require `ros-{distro}-pybind11-catkin` to be installed. You can disabled them with `catkin config --append --cmake-args -DSDF_TOOLS_PYTHON_BINDINGS=OFF`.
To test that the python bindings are working, you can run `./test/test_bindings.py`

# Implementation details

The low level distance field calculation can be found in sdf_generation.hpp, `BuildDistanceField`. The SDF is created by first creating two distance fields, one to free voxel and one to filled voxels, then combining them.