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Rethinking Voxelization and Classification for 3D Object Detection

2023-01-10 16:22:04
Youshaa Murhij, Alexander Golodkov, Dmitry Yudin
arXiv_AI
                 
Abstract

The main challenge in 3D object detection from LiDAR point clouds is achieving real-time performance without affecting the reliability of the network. In other words, the detecting network must be confident enough about its predictions. In this paper, we present a solution to improve network inference speed and precision at the same time by implementing a fast dynamic voxelizer that works on fast pillar-based models in the same way a voxelizer works on slow voxel-based models. In addition, we propose a lightweight detection sub-head model for classifying predicted objects and filter out false detected objects that significantly improves model precision in a negligible time and computing cost. The developed code is publicly available at: this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2301.04058

PDF

https://arxiv.org/pdf/2301.04058.pdf

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