GeoEdgeFormer: 3D point cloud saliency detection via edge-enhanced graph-transformer network

Publication Type

Journal Article

Publication Date

1-2026

Abstract

The goal of point cloud salient object detection is to identify and segment the most prominent areas or objects within a 3D point cloud. Research on point cloud SOD is still in its early stages, and many existing methods fail to fully utilize the rich geometric information inherent in point clouds. To address this limitation, we introduce GeoEdgeFormer, an effective Edge-Enhanced Transformer Network, designed specifically for 3D salient object detection. GeoEdgeFormer employs an encoder-decoder architecture featuring two novel components: the Residual Edge Convolution (REC) and the Global Contextual Transformer (GCT). In the encoder, we propose the REC, which is designed to maintain permutation invariance while capturing local geometric information. This component not only improves the model's ability to process complex point cloud data but also enhances its efficiency, making it suitable for dynamic. In the decoder, we introduce the GCT to learn scene-level contextual representations. The GCT integrates global semantics and multi-level features from the encoder into a cohesive global scene context. By effectively combining features from local and global levels, the model achieves a more comprehensive understanding of the scene's semantics, thereby enhancing its generalization ability. Extensive experiments on the PCSOD saliency dataset demonstrate that our proposed GeoEdgeFormer achieves state-of-the-art performance.

Keywords

3D salient object detection, Point cloud, Transformer

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Multimedia

Volume

28

First Page

5437

Last Page

5449

ISSN

1520-9210

Identifier

10.1109/TMM.2026.3668301

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TMM.2026.3668301

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