Semantic segmentation for robotic systems can enable a wide range of applications, from self-driving cars to augmented reality systems. LiDAR scans can have various different characteristics and properties, such as number of beams, vertical FoV, angular resolution. Our method provides a framework to seamlessly train on pointcloud data from various different LiDAR sensor models and types.
S2AE was written using PyTorch (http://pytorch.org/) and depends on a few libraries.
- s2cnn: https://github.com/jonas-koehler/s2cnn
- lie_learn: https://github.com/AMLab-Amsterdam/lie_learn
- pynvrtc: https://github.com/NVIDIA/pynvrtc
Submodule references to these repositories can be found in the deps folder
Our paper is available at
Bernreiter, Lukas, Lionel Ott, Roland Siegwart, and Cesar Cadena. "SphNet: A Spherical Network for Semantic Pointcloud Segmentation" [ArXiv]