Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network

TL;DR

This paper introduces a multi-layer transformer network for few-shot 3D point cloud semantic segmentation that leverages class-specific support features without pooling, achieving state-of-the-art results with reduced inference time.

Contribution

It proposes a novel transformer-based approach that effectively utilizes support features at multiple scales without pooling, improving accuracy and efficiency in few-shot segmentation.

Findings

Achieves state-of-the-art performance on S3DIS and ScanNet datasets.

Reduces inference time by 15% compared to existing models.

Effectively leverages support features for fine-grained segmentation.

Abstract

3D point cloud semantic segmentation aims to group all points into different semantic categories, which benefits important applications such as point cloud scene reconstruction and understanding. Existing supervised point cloud semantic segmentation methods usually require large-scale annotated point clouds for training and cannot handle new categories. While a few-shot learning method was proposed recently to address these two problems, it suffers from high computational complexity caused by graph construction and inability to learn fine-grained relationships among points due to the use of pooling operations. In this paper, we further address these problems by developing a new multi-layer transformer network for few-shot point cloud semantic segmentation. In the proposed network, the query point cloud features are aggregated based on the class-specific support features in different scales. Without using pooling operations, our method makes full use of all pixel-level features from the support samples. By better leveraging the support features for few-shot learning, the proposed method achieves the new state-of-the-art performance, with 15\% less inference time, over existing few-shot 3D point cloud segmentation models on the S3DIS dataset and the ScanNet dataset.