@inproceedings{77b22deb47ae4ef5941d3bb41bd5fc39,
title = "GraVoS: Voxel Selection for 3D Point-Cloud Detection",
abstract = "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.",
keywords = "Autonomous driving",
author = "Oren Shrout and Yizhak Ben-Shabat and Ayellet Tal",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 ; Conference date: 18-06-2023 Through 22-06-2023",
year = "2023",
doi = "10.1109/CVPR52729.2023.02077",
language = "الإنجليزيّة",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
pages = "21684--21693",
booktitle = "Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023",
}