JSPNet: Learning joint semantic & instance segmentation of point clouds via feature self-similarity and cross-task probability

Abstract

In this paper, we propose a novel method named JSPNet, to segment 3D point cloud in semantic and instance simultaneously. First, we analyze the problem in addressing joint semantic and instance segmentation, including the common ground of cooperation of two tasks, conflict of two tasks, quadruplet relation between semantic and instance distributions, and ignorance of existing works. Then we introduce our method to reinforce mutual cooperation and alleviate the essential conflict. Our method has a shared encoder and two decoders to address two tasks. Specifically, to maintain discriminative features and characterize inconspicuous content, a similarity-based feature fusion module is designed to locate the inconspicuous area in the feature of current branch and then select related features from the other branch to compensate for the unclear content. Furthermore, given the salient semantic feature and the salient instance feature, a cross-task probability-based feature fusion module is developed to establish the probabilistic correlation between semantic and instance features. This module could transform features from one branch and further fuse them with the other branch by multiplying probabilistic matrix. Experimental results on a large-scale 3D indoor point cloud dataset S3DIS and a part-segmentation dataset ShapeNet have demonstrated the superiority of our method over existing state-of-the-arts in both semantic and instance segmentation. The proposed method outperforms PointNet with 12% and 26% improvements and outperforms ASIS with 2.7% and 4.3% improvements in terms of mIoU and mPre. Code of this work has been made available at https://github.com/Chenfeng1271/JSPNet.