Work

Bidirectional Prototype Learning With Differential Attention Denoising for Few‐Shot Point Cloud Segmentation

Year: 2026

Type: article

Abstract: Semantic segmentation of 3D point clouds is constrained by the difficulty of obtaining labelled data. Few‐shot segmentation methods reduce the reliance on large‐scale annotations, but its performance is often constrained by the quality of prototypes, which can be degraded by noise and object feature discrepancies. To address this, we propose a Bidirectional Prototype Learning with Differential Attention Denoising (BPDA) framework. Our Differential Attention Denoising (DAD) module enhances prototype adaptation by suppressing irrelevant features and emphasizing critical ones. We further introduce a Bidirectional Prototype Learning mechanism, which iteratively refines prototypes by leveraging query predictions to generate new prototypes and inversely predict the class of support features. Furthermore, we incorporate a KL divergence loss to adjust the prototypes learning process through consistency constraints. Extensive experiments on S3DIS and ScanNet benchmark datasets demonstrate that our approach achieves competitive performance with state‐of‐the‐art methods.

Source: Computer Graphics Forum

DOI: 10.1111/cgf.70595

Authors: Xindan Zhang, Xindan Zhang, Ying Li, Xinnian Zhang, Xinnian Zhang

Institutions: Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Ajou University

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: Natural Science Foundation of Jilin Province, People's Government of Jilin Province

Award: G1308185617