Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
This work addresses the challenge of direct semantic segmentation in 3D point clouds for applications like robotics and autonomous driving, representing an incremental improvement over existing methods.
The paper tackles the problem of 3D semantic segmentation of unstructured point clouds by extending PointNet to incorporate larger-scale spatial context, showing improved results on indoor and outdoor datasets.
Deep learning approaches have made tremendous progress in the field of semantic segmentation over the past few years. However, most current approaches operate in the 2D image space. Direct semantic segmentation of unstructured 3D point clouds is still an open research problem. The recently proposed PointNet architecture presents an interesting step ahead in that it can operate on unstructured point clouds, achieving encouraging segmentation results. However, it subdivides the input points into a grid of blocks and processes each such block individually. In this paper, we investigate the question how such an architecture can be extended to incorporate larger-scale spatial context. We build upon PointNet and propose two extensions that enlarge the receptive field over the 3D scene. We evaluate the proposed strategies on challenging indoor and outdoor datasets and show improved results in both scenarios.