FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation

Understand

Scene understanding based on LiDAR point cloud is an essential task for autonomous cars to drive safely, which often employs spherical projection to map 3D point cloud into multi-channel 2D images for semantic segmentation.

• Most existing methods simply stack different point attributes/modalities (e.g.
• coordinates, intensity, depth, etc.) as image channels to increase information capacity, but ignore distinct characteristics of point attributes in different image channels.
• We design FPS-Net, a convolutional fusion network that exploits the uniqueness and discrepancy among the projected image channels for optimal point cloud segmentation.