Multi-scale Network with Attentional Multi-resolution Fusion for Point Cloud Semantic Segmentation

TL;DR

This paper introduces a multi-scale point cloud segmentation network that combines local and global features using novel modules and attention fusion, achieving state-of-the-art results on benchmark datasets.

Contribution

The paper proposes a new network architecture with ACPConv, MSS blocks, and a customized HRNet for effective multi-scale feature learning in point cloud segmentation.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively combines local and global features through novel modules.

Demonstrates the effectiveness of point-wise attention fusion.

Abstract

In this paper, we present a comprehensive point cloud semantic segmentation network that aggregates both local and global multi-scale information. First, we propose an Angle Correlation Point Convolution (ACPConv) module to effectively learn the local shapes of points. Second, based upon ACPConv, we introduce a local multi-scale split (MSS) block that hierarchically connects features within one single block and gradually enlarges the receptive field which is beneficial for exploiting the local context. Third, inspired by HRNet which has excellent performance on 2D image vision tasks, we build an HRNet customized for point cloud to learn global multi-scale context. Lastly, we introduce a point-wise attention fusion approach that fuses multi-resolution predictions and further improves point cloud semantic segmentation performance. Our experimental results and ablations on several benchmark datasets show that our proposed method is effective and able to achieve state-of-the-art performances compared to existing methods.