V2PNet: Voxel-to-Point Feature Propagation and Fusion That Improves Feature Representation for Point Cloud Registration

Abstract

Point-based and voxel-based methods can learn the local features of point clouds. However, although point-based methods are geometrically precise, the discrete nature of point clouds negatively affects feature learning performance. Moreover, although voxel-based methods can exploit the learning power of convolutional neural networks, their resolution and detail extraction may be inadequate. Therefore, in this study, point-based and voxel-based methods are combined to enhance localization precision and matching distinctiveness. The core procedure is embodied in V2PNet, an innovative fused neural network that we design to perform voxel-to-pixel propagation and fusion, which seamlessly integrates the two encoder–decoder branches. Experiments are conducted on indoor and outdoor benchmark datasets with different platforms and sensors, i.e., the 3DMatch and Karlsruhe Institute of Technology and Toyota Technological Institute datasets, with the registration recall of 89.4% and the success rate of 99.86%, respectively. Qualitative and quantitative evaluations demonstrate that V2PNet has shown improvements in semantic awareness, geometric structure discernment, and other performance metrics.

Keywords

• 3-D feature point
• 3-D metric learning
• point cloud registration
• sparse convolution