FA-ResNet: Feature affine residual network for large-scale point cloud segmentation

Abstract

Semantic segmentation for large-scale point clouds in 3D computer vision remains challenging. Most existing studies focus on creating complex local geometry extractors without considering the sparsity of point clouds and the multi-scale problem of objects in large-scale, resulting in networks that fail to efficiently extract local features and affect segmentation accuracy. In this study, we propose a novel Feature Affine Residual (FA-Res) learnable module for this problem to learn robust point cloud semantic information from the point cloud domain. First, we create a Local Relation-shape Learning module to learn local shape relationships, thereby supplementing the structural information. Second, we propose a lightweight Feature Affine module that can conduct adaptive modifications on local point features to reduce differences between local point clouds with varying densities and the K-nearest neighbor (KNN) algorithm’s domain determination. Finally, we design a Residual MLP Pooling module that can learn deep aggregation features to explore more sophisticated semantic data and provide better guidance for semantic segmentation. We compare our network with state-of-the-art networks on two separate datasets to show its effectiveness. Specifically, our method achieves 68.1% mIoU on S3DIS tested on Area 5, which is an improvement of 2.7% compared with the latest representative network.

Keywords

• Point clouds
• 3D computer vision
• Semantic segmentation
• Residual MLP
• Feature affine