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

12 May 2023OpenReview Archive Direct UploadReaders: Everyone
Abstract: 3D point cloud semantic segmentation aims to group all points into different semantic categories, which benefits im- portant 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 computa- tional 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 ag- gregated based on the class-specific support features in dif- ferent 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
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