GraVoS: Voxel Selection for 3D Point-Cloud Detection

Fuente: arXiv
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Autori principali: Shrout, Oren, Ben-Shabat, Yizhak, Tal, Ayellet
Natura: Preprint
Pubblicazione: 2022
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author Shrout, Oren
Ben-Shabat, Yizhak
Tal, Ayellet
author_facet Shrout, Oren
Ben-Shabat, Yizhak
Tal, Ayellet
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2208_08780
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle GraVoS: Voxel Selection for 3D Point-Cloud Detection
Shrout, Oren
Ben-Shabat, Yizhak
Tal, Ayellet
Computer Vision and Pattern Recognition
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.
title GraVoS: Voxel Selection for 3D Point-Cloud Detection
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2208.08780