SDSimPoint: Shallow–Deep Similarity Learning for Few-Shot Point Cloud Semantic Segmentation
Published in TNNLS, 2025
Authors: Jiahui Wang, Haiyue Zhu , Haoren Guo , Abdullah Al Mamun , Cheng Xiang , Clarence W de Silva , Tong Heng Lee
Three-dimensional point cloud semantic segmentation is a core task in computer vision. While few-shot methods address limited-data scenarios better than fully supervised approaches, they often struggle to capture class-specific features due to class-agnostic pretraining. To overcome this, we propose SDSimPoint, a shallow–deep similarity learning network that models both shallow (e.g., geometry, color) and deep (e.g., context, semantics) similarities between support and query samples. We further introduce BEAM (Beyond-Episode Attention Module), which expands attention beyond a single episode using memory units, enhancing similarity modeling. Additionally, our learnable distance metric adapts to complex data distributions. SDSimPoint consistently outperforms baselines across multiple datasets in few-shot point cloud segmentation. Read more