Guo, Q., Wang, Z., Wang, L., Dong, H., Juefei-Xu, F., Lin, D., Ma, L., Feng, W., & Liu, Y. (2025). CarveNet: Carving Point-Block for Complex 3D Shape Completion. IEEE Transactions on Multimedia, 27, 1047–1058. https://doi.org/10.1109/tmm.2024.3443613
Abstract:
3D point cloud completion is very challenging because it relies on accurately understanding 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 a 3D block that contains the uniformly distributed 3D points, yielding the entire point cloud. We propose a new network architecture to achieve PC, i.e., CarveNet. This network conducts the exclusive convolution on each block point, where the convolutional kernels are trained on the 3D shape data. CarveNet determines which point should be carved to recover the complete shapes’ details effectively. 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, ShapNet-55/34 and KITTI datasets demonstrate the generality of our approach on the partial point clouds with diverse patterns. On these datasets, CarveNet successfully outperforms the state-of-the-art methods.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority - Trust Tech Funding Initiative
Grant Reference no. : DTC-RGC-04
This research / project is supported by the National Research Foundation, Singapore, and DSO National Laboratories - AI Singapore Programme
Grant Reference no. : AISG2-GC-2023-008
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Career Development Fund (CDF)
Grant Reference no. : C233312028