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GraVoS: Gradient based Voxel Selection for 3D Detection

2022-08-18 11:29:43
Oren Shrout, Yizhak Ben-Shabat, Ayellet Tal
arXiv_CV
             
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 the extreme foreground-background imbalance in the scene 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 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.

Abstract (translated)

URL

https://arxiv.org/abs/2208.08780

PDF

https://arxiv.org/pdf/2208.08780.pdf

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