3D object detection within large 3D scenes is challenging not only due to the sparsity and irregularity of 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.
@article{arxiv.2208.08780,
title = {GraVoS: Voxel Selection for 3D Point-Cloud Detection},
author = {Oren Shrout and Yizhak Ben-Shabat and Ayellet Tal},
journal= {arXiv preprint arXiv:2208.08780},
year = {2024}
}