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C2SPoint: A classification-to-saliency network for point cloud saliency detection

Jiang, Zhaoyi; Ding, Luyun; Tam, Gary; Song, Chao; Li, Frederick W.B.; Yang, Bailin


Zhaoyi Jiang

Luyun Ding

Gary Tam

Chao Song

Bailin Yang


Point cloud saliency detection is an important technique that support downstream tasks in 3D graphics and vision, like 3D model simplification, compression, reconstruction and viewpoint selection. Existing approaches often rely on hand-crafted features and are only applicable to specific datasets. In this paper, we propose a novel weakly supervised classification network, called C2SPoint, which directly performs saliency detection on the point clouds. Unlike previous methods that require per-point saliency annotations, C2SPoint only requires category labels of the point clouds during training. The network consists of two branches: a Classification branch and a Saliency branch. The former branch is composed of two Adaptive Set Abstraction layers for feature extraction and a Saliency Transform layer for learning saliency knowledge from the classification network. The latter branch introduces a multi-scale point-cluster similarity matrix for propagating the cluster saliency to each point within it, resulting in the prediction of point-level saliency. Experimental results demonstrate the effectiveness of our method in point cloud saliency detection, with improvements of 2% in both AUC and NSS compared to state-of-the-art methods.

Journal Article Type Article
Acceptance Date Jul 3, 2023
Online Publication Date Jul 8, 2023
Publication Date 2023
Deposit Date Jul 14, 2023
Publicly Available Date Jul 9, 2024
Journal Computers & Graphics
Print ISSN 0097-8493
Publisher Elsevier
Peer Reviewed Peer Reviewed
Public URL


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