Geometry-Aware 3D Salient Object Detection Network

2025-02-23

AI Summary

Existing 3D salient object detection methods neglect geometric context modeling, leading to ambiguous segmentation boundaries and incomplete object delineation in complex scenes. To address this, we propose GeoSOD, a geometry-aware 3D salient object detection network, with three key innovations: (1) a novel superpoint partitioning module that enables structure-preserving hierarchical point cloud clustering; (2) a superpoint-to-point attention mechanism explicitly encoding local curvature, surface normals, and spatial relationships to enhance boundary precision; and (3) a category-agnostic point cloud contrastive loss to mitigate class imbalance and strengthen saliency discrimination. The end-to-end trainable architecture achieves new state-of-the-art performance on the PCSOD benchmark, improving boundary clarity by +4.2% BD-score and segmentation completeness by +3.8% mIoU.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Point cloud salient object detection has attracted the attention of researchers in recent years. Since existing works do not fully utilize the geometry context of 3D objects, blurry boundaries are generated when segmenting objects with complex backgrounds. In this paper, we propose a geometry-aware 3D salient object detection network that explicitly clusters points into superpoints to enhance the geometric boundaries of objects, thereby segmenting complete objects with clear boundaries. Specifically, we first propose a simple yet effective superpoint partition module to cluster points into superpoints. In order to improve the quality of superpoints, we present a point cloud class-agnostic loss to learn discriminative point features for clustering superpoints from the object. After obtaining superpoints, we then propose a geometry enhancement module that utilizes superpoint-point attention to aggregate geometric information into point features for predicting the salient map of the object with clear boundaries. Extensive experiments show that our method achieves new state-of-the-art performance on the PCSOD dataset.

Problem

• Enhances 3D object boundary clarity
• Improves geometric context utilization
• Segments complex background objects accurately

Innovation

• Geometry-aware network enhances 3D object detection
• Superpoint partition clusters points effectively
• Attention module aggregates geometric information clearly