# A Density-Aware PointRCNN for 3D Object Detection in Point Clouds

Source: [A Density-Aware PointRCNN for 3D Object Detection in Point Clouds](<https://www.sota2.com/research/paper/68fa690b1e209131cea1fafe>)

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

Data updated: 2025-10-25T22:51:52.067Z

Authors: Jie Li, Yu Hu

Published: 2020-09-11T09:35:13.000Z

## Abstract

We present an improved version of PointRCNN for 3D object detection, in which a multi-branch backbone network is adopted to handle the non-uniform density of point clouds. An uncertainty-based sampling policy is proposed to deal with the distribution differences of different point clouds. The new model can achieve about 0.8 AP higher performance than the baseline PointRCNN on KITTI val set. In addition, a simplified model using a single scale grouping for each set-abstraction layer can achieve competitive performance with less computational cost.

## 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/2009.05307>)

[PDF](<https://arxiv.org/pdf/2009.05307.pdf>)