Weakly supervised point cloud semantic segmentation with the fusion of heterogeneous network features

Abstract

Weakly supervised point cloud segmentation has emerged as a prominent research area to address the problem of manual annotation costs. A crucial challenge in weakly supervised point cloud segmentation is the implicit augmentation of the total amount of supervision signals. In this article, we propose a novel method that utilizes the fusion of features from different networks to enhance the supervision signals. Specifically, we utilize a deep Encoder-Decoder network to capture high-level semantic features of labeled points, while a shallow Encoder network captures multi-scale detail features of labeled data. By combining these two heterogeneous networks, we acquire richer feature representations that implicitly enhance the supervision signal.Furthermore, we introduce scene-level and instance-level contrast to enhance feature representations in both coarse-grained and fine-grained manners, thus further boosting the supervisory signal. To validate the effectiveness of our approach, we conducted experiments on the large-scale indoor scene dataset, S3DIS, and the outdoor datasets, Toronto3D and Semantic3D, achieving convincing results.