# Rethinking Voxelization and Classification for 3D Object Detection

Source: [Rethinking Voxelization and Classification for 3D Object Detection](<https://www.sota2.com/research/paper/68fa6e571e209131cea3f256>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:55:03.570Z

Authors: Youshaa Murhij, Alexander Golodkov, Dmitry Yudin

Published: 2023-01-10T16:22:04.000Z

## 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: https://github.com/YoushaaMurhij/RVCDet.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2301.04058>)

[PDF](<https://arxiv.org/pdf/2301.04058.pdf>)