JSMNet Improving Indoor Point Cloud Semantic and Instance Segmentation through Self-Attention and Multiscale

TL;DR

JSMNet is a novel deep learning framework that enhances indoor 3D point cloud semantic and instance segmentation by integrating self-attention and multiscale features, achieving significant accuracy improvements.

Contribution

The paper introduces JSMNet, combining self-attention and multiscale fusion modules for superior indoor point cloud segmentation, outperforming existing methods on S3DIS dataset.

Findings

Outperforms PointNet by 16.0% in semantic mIoU and 26.3% in instance mPre.

Achieves better segmentation results than ASIS and JSPNet.

Provides improved target local area segmentation in indoor environments.

Abstract

The semantic understanding of indoor 3D point cloud data is crucial for a range of subsequent applications, including indoor service robots, navigation systems, and digital twin engineering. Global features are crucial for achieving high-quality semantic and instance segmentation of indoor point clouds, as they provide essential long-range context information. To this end, we propose JSMNet, which combines a multi-layer network with a global feature self-attention module to jointly segment three-dimensional point cloud semantics and instances. To better express the characteristics of indoor targets, we have designed a multi-resolution feature adaptive fusion module that takes into account the differences in point cloud density caused by varying scanner distances from the target. Additionally, we propose a framework for joint semantic and instance segmentation by integrating semantic and instance features to achieve superior results. We conduct experiments on S3DIS, which is a large three-dimensional indoor point cloud dataset. Our proposed method is compared against other methods, and the results show that it outperforms existing methods in semantic and instance segmentation and provides better results in target local area segmentation. Specifically, our proposed method outperforms PointNet (Qi et al., 2017a) by 16.0% and 26.3% in terms of semantic segmentation mIoU in S3DIS (Area 5) and instance segmentation mPre, respectively. Additionally, it surpasses ASIS (Wang et al., 2019) by 6.0% and 4.6%, respectively, as well as JSPNet (Chen et al., 2022) by a margin of 3.3% for semantic segmentation mIoU and a slight improvement of 0.3% for instance segmentation mPre.