Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics. (arXiv:2211.10580v1 [cs.CV])

High-quality estimation of surface normal can help reduce ambiguity in many
geometry understanding problems, such as collision avoidance and occlusion
inference. This paper presents a technique for estimating the normal from 3D
point clouds and 2D colour images. We have developed a transformer neural
network that learns to utilise the hybrid information of visual semantic and 3D
geometric data, as well as effective learning strategies. Compared to existing
methods, the information fusion of the proposed method is more effective, which …

arxiv geometry lidar normal semantics transformer