# Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection

Source: [Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection](<https://www.sota2.com/research/paper/68fa72671e209131cea56487>)

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

Data updated: 2025-10-26T02:25:11.364Z

Authors: Shitao Chen, Haolin Zhang, Nanning Zheng

Published: 2024-03-04T12:20:40.000Z

## Abstract

3D object detection based on LiDAR point cloud and prior anchor boxes is a critical technology for autonomous driving environment perception and understanding. Nevertheless, an overlooked practical issue in existing methods is the ambiguity in training sample allocation based on box Intersection over Union (IoU\_box). This problem impedes further enhancements in the performance of anchor-based LiDAR 3D object detectors. To tackle this challenge, this paper introduces a new training sample selection method that utilizes point cloud distribution for anchor sample quality measurement, named Point Assisted Sample Selection (PASS). This method has undergone rigorous evaluation on two widely utilized datasets. Experimental results demonstrate that the application of PASS elevates the average precision of anchor-based LiDAR 3D object detectors to a novel state-of-the-art, thereby proving the effectiveness of the proposed approach. The codes will be made available at https://github.com/XJTU-Haolin/Point\_Assisted\_Sample\_Selection.

## 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/2403.01978>)

[PDF](<https://arxiv.org/pdf/2403.01978.pdf>)