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

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. • We propose C2SPoint, a novel neural network for 3D point cloud saliency detection. • It leverages only point cloud category labels without the need for point-wise saliency annotations. • A multi-scale point-cluster similarity matrix is introduced to predict point-level saliency from cluster saliency. • The experimental results demonstrate that the proposed method outperforms the state-of-the-art algorithms. [Display omitted] [ABSTRACT FROM AUTHOR]