A Multimodal 3D Object Detection Method with Integrated Statistical Features

CHU Huazhen; XIE Tian; PAN Bei; TAN Senqi; SU Xiangyang; WANG Yang

doi:10.12382/bgxb.2025.0578

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A Multimodal 3D Object Detection Method with Integrated Statistical Features

更新时间：2026-05-06

- A Multimodal 3D Object Detection Method with Integrated Statistical Features
- Acta Armamentarii Vol. 47, Issue 4, Pages: 250578(2026)
- 作者机构：
  
  中兵智能创新研究院有限公司，北京 100072
  群体协同与自主实验室，北京 100072
  中国北方车辆研究所，北京 100072
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0578
  CLC： TP391.4
- Received：30 June 2025，
  
  Online First：25 December 2025，
  
  Published：2026-04
- 稿件说明：
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CHU Huazhen, XIE Tian, PAN Bei, et al. A Multimodal 3D Object Detection Method with Integrated Statistical Features[J]. Acta Armamentarii, 2026, 47(4): 250578.
DOI：

CHU Huazhen, XIE Tian, PAN Bei, et al. A Multimodal 3D Object Detection Method with Integrated Statistical Features[J]. Acta Armamentarii, 2026, 47(4): 250578. DOI： 10.12382/bgxb.2025.0578.

摘要

三维目标检测是自动驾驶与军事智能感知的关键环节，其性能直接影响复杂环境下的态势认知与决策质量。尽管多模态融合取得进展，但弱纹理目标识别、稀疏点云表征及跨模态配准误差仍构成主要瓶颈。针对上述问题，提出一种融合统计特征的多模态三维目标检测框架，旨在增强模型对真实复杂环境中多样化场景的适应能力和鲁棒性。通过构建精细的点云与图像之间的投影映射实现点级语义特征的有效获取；在体素化过程中引入局部点云的均值与标准差作为统计先验，显著提升特征表示的一致性和稳定性。融合后的多模态特征被输入至三维检测主干网络，完成目标的精确识别与定位。基于KITTI数据集的实验结果显示，新方法在小目标检测任务中表现尤为突出，平均精度较现有优秀模型PV-RCNN提升0.87%，验证了新方法在复杂场景下的实用性和稳健性，为三维目标检测技术的发展提供了新的思路与方法支持。

Abstract

3D object detection is a critical component of autonomous driving and military intelligent perception

which directly shapes the situational awareness and decision-making in complex environments. The weak-texture target recognition

sparse point cloud representation and cross-modal registration errors remain major bottlenecks although progress has been made in multimodal fusion. To address the aforementioned issues

a multimodal 3D object detection framework that incorporates the statistical features is proposed for enhancing the model's adaptability and robustness to the diverse realworld complex scenarios. The point-level semantic features are effectively acquisited by constructing a refined projection mapping between point cloud and image. The mean and standard deviation of local point cloud as statistical priors are introduced during voxelization

significantly improving the consistency and stability of feature representations. The fused multimodal features are then fed into a 3D detection backbone network to achieve the precise recognition and localization of an object. The experimental results based on the KITTI dataset demonstrate that the proposed method performs particularly well in small object detection tasks

and improves the average detection accuracy by 0.87% compared to PV-RCNN model. These results validate the practicality and robustness of the proposed method in complex scenarios

providing new insights and methodological support for the advancement of 3D object detection technologies.

关键词

Keywords

references

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