PPSAN: Perceptual-aware 3D Point Cloud Segmentation via Adversarial Learning

Abstract

Point cloud segmentation is a key problem of 3D multimedia signal processing. Existing methods usually use a single network structure which is trained by a per-point loss. These methods mainly focus on the geometric similarity between the prediction results and the ground truth, ignoring visual perception difference. In this paper, we present a segmentation adversarial network to overcome the drawbacks above. A discriminator is introduced to provide a perceptual loss to increase the rationality judgment of prediction and guide the further optimization of the segmentator. In order to perfectly capture the structural information of parts in the same category of objects, condition settings are employed to add a global constraint. Experimental results show the proposed methods can correct the common errors in point cloud segmentation and obtain more accurate and better segmentation of visual perceptual.