Late-decoupled 3D Hierarchical Semantic Segmentation with Semantic Prototype Discrimination based Bi-branch Supervision

TL;DR

This paper introduces a late-decoupled 3D hierarchical semantic segmentation framework with semantic prototype discrimination, effectively addressing multi-hierarchy conflicts and class imbalance, leading to state-of-the-art results in 3D scene understanding.

Contribution

The paper proposes a novel late-decoupled architecture with a bi-branch supervision mechanism for improved 3D hierarchical segmentation.

Findings

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

Effectively mitigates multi-hierarchy conflicts and class imbalance.

Core components can enhance existing methods as plug-and-play modules.

Abstract

3D hierarchical semantic segmentation (3DHS) is crucial for embodied intelligence applications that demand a multi-grained and multi-hierarchy understanding of 3D scenes. Despite the progress, previous 3DHS methods have overlooked following two challenges: I) multi-label learning with a parameter-sharing model can lead to multi-hierarchy conflicts in cross-hierarchy optimization, and II) the class imbalance issue is inevitable across multiple hierarchies of 3D scenes, which makes the model performance become dominated by major classes. To address these issues, we propose a novel framework with a primary 3DHS branch and an auxiliary discrimination branch. Specifically, to alleviate the multi-hierarchy conflicts, we propose a late-decoupled 3DHS framework which employs multiple decoders with the coarse-to-fine hierarchical guidance and consistency. The late-decoupled architecture can mitigate the underfitting and overfitting conflicts among multiple hierarchies and can also constrain the class imbalance problem in each individual hierarchy. Moreover, we introduce a 3DHS-oriented semantic prototype based bi-branch supervision mechanism, which additionally learns class-wise discriminative point cloud features and performs mutual supervision between the auxiliary and 3DHS branches, to enhance the class-imbalance segmentation. Extensive experiments on multiple datasets and backbones demonstrate that our approach achieves state-of-the-art 3DHS performance, and its core components can also be used as a plug-and-play enhancement to improve previous methods.