A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues

Abstract

In order to track dynamic objects in a robot's environment, one must first segment the scene into a collection of separate objects. Most real-time robotic vision systems today rely on simple spatial relations to segment the scene into separate objects. However, such methods fail under a variety of realworld situations such as occlusions or crowds of closely-packed objects. We propose a probabilistic 3D segmentation method that combines spatial, temporal, and semantic information to make better-informed decisions about how to segment a scene. We begin with a coarse initial segmentation. We then compute the probability that a given segment should be split into multiple segments or that multiple segments should be merged into a single segment, using spatial, semantic, and temporal cues. Our probabilistic segmentation framework enables us to significantly reduce both undersegmentations and oversegmentations on the KITTI dataset By combining spatial, temporal, and semantic information, we are able to create a more robust 3D segmentation system that leads to better overall perception in crowded dynamic environments.