Can I read CarveNet: Carving Point-Block for Complex 3D Shape Completion on EtoBox?

CarveNet: Carving Point-Block for Complex 3D Shape Completion by Guo, Qing; Wang, Zhijie; Juefei-Xu, Felix; Lin, Di; Ma, Lei; Feng, Wei; Liu, Yang is a scholarly article available to read on EtoBox.

What is CarveNet: Carving Point-Block for Complex 3D Shape Completion about?

3D point cloud completion is very challenging because it heavily relies on the accurate understanding of the complex 3D shapes (e.g., high-curvature, concave/convex, and hollowed-out 3D shapes) and the unknown & diverse patterns of the partially available point clouds. In this paper, we propose a novel solution,i.e., Point-block Carving (PC), for completing the complex 3D point cloud completion. Given the partial point cloud as the guidance, we carve a3D block that contains the uniformly distributed 3D points, yielding the entire point cloud. To achieve PC, we propose a new network architecture, i.e., CarveNet. This network conducts the exclusive convolution on each point of the block, where the convolutional kernels are trained on the 3D shape data. CarveNet determines which point should be carved, for effectively recovering the details of the complete shapes. Furthermore, we propose a sensor-aware method for data augmentation,i.e., SensorAug, for training CarveNet on richer patterns of partial point clouds, thus enhancing the completion power of the network. The extensive evaluations on the ShapeNet and KITTI datasets demonstrate the generality of our approach on the partial poin

Author

Guo, Qing; Wang, Zhijie; Juefei-Xu, Felix; Lin, Di; Ma, Lei; Feng, Wei; Liu, Yang

Published

2021

Language

EN