  Common image-based LiDAR point cloud semantic segmentation (LiDAR PCSS)
approaches have bottlenecks resulting from the boundary-blurring problem of
convolution neural networks (CNNs) and quantitation loss of spherical
projection. In this work, we propose a transformer-based plug-and-play
uncertain point refiner, i.e., TransUPR, to refine selected uncertain points in
a learnable manner, which leads to an improved segmentation performance.
Uncertain points are sampled from coarse semantic segmentation results of 2D
image segmentation where uncertain points are located close to the object
boundaries in the 2D range image representation and 3D spherical projection
background points. Following that, the geometry and coarse semantic features of
uncertain points are aggregated by neighbor points in 3D space without adding
expensive computation and memory footprint. Finally, the transformer-based
refiner, which contains four stacked self-attention layers, along with an MLP
module, is utilized for uncertain point classification on the concatenated
features of self-attention layers. As the proposed refiner is independent of 2D
CNNs, our TransUPR can be easily integrated into any existing image-based LiDAR
PCSS approaches, e.g., CENet. Our TransUPR with the CENet achieves
state-of-the-art performance, i.e., 68.2% mean Intersection over Union (mIoU)
on the Semantic KITTI benchmark, which provides a performance improvement of
0.6% on the mIoU compared to the original CENet.
