App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 12

Multimodal 3d object detection based on quality-aware cross-modal Fusion

Lu Bin1,2
Hao Dongning1,2
Wang Minghan1,2
1. Dept. of Computer Science, North China Electric Power University, Baoding Hebei 071003, China
2. Key Laboratory of Energy and Electric Power Knowledge Calculation in Hebei Province, Hebei Baoding 071003, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0094

Abstract

This study proposes a Quality-Aware Cross-Modal Residual Fusion (QA-CMRF) network to address insufficient reliability modeling of pseudo-point-cloud features and limited discriminability in multimodal 3D object detection. The method takes LiDAR point clouds and image-derived pseudo point clouds as inputs. It generates dense pseudo point clouds using TWISE depth completion and builds a confidence-guided pseudo-point-cloud feature recalibration module before and after RoI voxel pooling to model pseudo-point-cloud reliability at both point and voxel levels. It then constructs a cross-modal residual-aware fusion module in the aligned RoI voxel feature space to explicitly compute the response discrepancy between LiDAR and pseudo-point-cloud features. Experiments on the KITTI car class under IoU=0.7 show that QA-CMRF achieves 3D AP_R40 values of 96.01%, 88.94%, and 86.05% on the Easy, Moderate, and Hard difficulty levels, respectively, improving SFD++ by 0.27, 0.14, and 0.01 percentage points. The results show that the proposed method improves pseudo-point-cloud quality modeling and multimodal 3D detection performance.

Foundation Support

北京市自然科学基金资助(4254105)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0094
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 12

Publish History

[2026-07-29] Accepted Paper

Cite This Article

鲁斌, 郝冬宁, 王明晗. 基于质量感知跨模态融合的多模态三维目标检测方法 [J]. 计算机应用研究, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0094. (Lu Bin, Hao Dongning, Wang Minghan. Multimodal 3d object detection based on quality-aware cross-modal Fusion [J]. Application Research of Computers, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0094. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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