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[bibtex]@InProceedings{Shrout_2023_CVPR, author = {Shrout, Oren and Ben-Shabat, Yizhak and Tal, Ayellet}, title = {GraVoS: Voxel Selection for 3D Point-Cloud Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {21684-21693} }
GraVoS: Voxel Selection for 3D Point-Cloud Detection
Abstract
3D object detection within large 3D scenes is challenging not only due to the sparse and irregular 3D point clouds, but also due to both the extreme foreground-background scene imbalance and class imbalance. A common approach is to add ground-truth objects from other scenes. Differently, we propose to modify the scenes by removing elements (voxels), rather than adding ones. Our approach selects the "meaningful" voxels, in a manner that addresses both types of dataset imbalance. The approach is general and can be applied to any voxel-based detector, yet the meaningfulness of a voxel is network-dependent. Our voxel selection is shown to improve the performance of several prominent 3D detection methods.
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