Learning global-view correlation for salient object detection in 3D point clouds

Abstract

Salient object detection (SOD) in point clouds has been an emerging research topic aimed at extracting most visually attractive objects from 3D point cloud representations. The inherent irregularity and unorderness of 3D point clouds complicate salient object detection, for it is hard to learn regular salient patterns like in 2D images. Meanwhile, existing methods typically focus on per-point context aggregation, while overlooking the scene-level global-view correlation crucial for saliency prediction. In this paper, we explore SOD in point clouds and introduce a novel approach that capitalizes on a comprehensive understanding of global-view 3D scenes. Our proposed method, the Saliency Filtration Network (SFN), meticulously refines saliency representations by isolating them from the common scene-dependent global-view correlations. Most importantly, SFN is characterized by a two-stage strategy, which involves aggregating long-range context information and purify saliency from globally scene-common correlations. To achieve this, we introduce the Residual Relation-aware Transformer module (RRT), which considers human visual perception to exploit global-view context dependencies. Additionally, we propose the Global Bilinear Correlation based Filtration module (GBCF) to perform saliency purification from global-view correlations. GBCF establishes dense correlations between global space and channel descriptors, which are then leveraged to properly purify saliency representations. Experimental evaluations on the PCSOD benchmark demonstrate that our proposed method achieves state-of-the-art accuracy and significantly outperforms other compared methods.