LiDAR Object Detection Method Based on Point Cloud Mask Pre-training and Gaussian Localization Uncertainty Estimation

Yu FENG; Guangda XIE; Long LIU; Yunquan SU; Junwei LIU; Yandong GENG; Lie MIAO

doi:10.12382/bgxb.2024.0788

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LiDAR Object Detection Method Based on Point Cloud Mask Pre-training and Gaussian Localization Uncertainty Estimation

更新时间：2025-09-05

- LiDAR Object Detection Method Based on Point Cloud Mask Pre-training and Gaussian Localization Uncertainty Estimation
- Acta Armamentarii Vol. 46, Issue 6, Pages: 240788(2025)
- 作者机构：
  
  内蒙古第一机械集团股份有限公司科研所, 内蒙古包头 014000
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.0788
  CLC：
- Received：03 September 2024，
  
  Published Online：28 June 2025，
  
  Published：10 June 2025
- 稿件说明：
移动端阅览
Yu FENG, Guangda XIE, Long LIU, et al. LiDAR Object Detection Method Based on Point Cloud Mask Pre-training and Gaussian Localization Uncertainty Estimation[J]. Acta Armamentarii, 2025, 46(6): 240788.
DOI：

Yu FENG, Guangda XIE, Long LIU, et al. LiDAR Object Detection Method Based on Point Cloud Mask Pre-training and Gaussian Localization Uncertainty Estimation[J]. Acta Armamentarii, 2025, 46(6): 240788. DOI： 10.12382/bgxb.2024.0788.

摘要

激光雷达获取的三维点云数据是自动驾驶的关键数据源

但其标注难度大、数据量有限且标签不确定性高

限制了基于深度学习的三维目标检测模型的训练效果。针对以上问题

提出点云掩码策略构建预训练数据集

结合迁移学习提升模型检测精度;提出基于高斯分布的定位不确定性估计建模方法

使目标检测模型在预测边界框坐标的同时预测每个坐标的定位不确定性。实验结果表明

在不明显增加算法复杂度的前提下

新方法有效减少了误检现象

显著提高了目标检测的准确性。

Abstract

The 3D point cloud data acquired by LiDAR is crucial for autonomous driving.However

the annotation of point cloud data is difficult;the available data is limited;and there is usually a high uncertainty in its labels

which constrain the training effects of deep learning-based 3D object detection models.To address these issues

this paper proposes a point cloud masking strategy to construct a pre-training dataset

which is combined with transfer learning to improve detection accuracy.Additionally

a Gaussian distribution-based localization uncertainty estimation modeling method is proposed

enabling the object detection model to predict the localization uncertainty of each coordinate while predicting the bounding box coordinates.Experimental results demonstrate that the proposed method effectively reduces the false detections and significantly improves the accuracy of object detection without significantly increasing algorithmic complexity.

关键词

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references

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