Cascaded Contextual Reasoning for Large-Scale Point Cloud Semantic Segmentation

Abstract

Large-scale point cloud semantic segmentation aims to efficiently process millions of points and classify each point into the correct class. Most traditional methods are designed for indoor scenes and are inpractical to process large-scale point clouds directly. To improve efficiency, appropriate point reduction strategies such as random sampling are necessary to reduce points significantly. However, it brings some problems. First, such reduction leads to serious information loss during encoding phase. Second, to recover from the reduction of points, low-level features need to be carefully fused after upsampling for refinement. To address above issues, we propose a Cascaded Contextual Reasoning network (PointCCR) for large-scale point cloud semantic segmentation. We propose a Dilated Graph Convolution (DGC) module to exploit local structure of point clouds and efficiently increase receptive fields. To make compensation for the information loss caused by reducing points, we propose a Cross-level Feature Enrichment (CFE) module which models cross-level contextual dependencies. DGC and CFE together constitute the Multi-level Cascade Aggregation (MCA) module as encoder. Furthermore, we build a Class-aware Attentive Refinement (CAR) module to selectively fuse low-level features for refinement after upsampling. Extensive experimental results demonstrate our method achieves superior performance compared with other state-of-the-arts on three large-scale point cloud datasets (i.e., Semantic3D, SemanticKITTI and S3DIS).

Keywords

• Large-scale point cloud
• semantic segmentation
• cascaded contextual reasoning