  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.
