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BRTPillar: boosting real-time 3d object detection based point cloud and RGB image fusion in autonomous driving

INTERNATIONAL JOURNAL OF INTELLIGENT COMPUTING ANd CYBERNETICS 2025年第1期18卷 217-235页

作者： Zhang, Zhitian Zhao, Hongdong Zhao, Yazhou Chen, dan Zhang, Ke Li, Yanqi Hebei Univ Technol Sch Elect & Informat Engn Tianjin Peoples R China

PurposeIn autonomous driving, the inherent sparsity of point clouds often limits the performance of object detection, while existing multimodal architectures struggle to meet the real-time requirements for 3d object detection. Therefore, the main purpose of this paper is to significantly enhance the detection performance of objects, especially the recognition capability for small-sized objects and to address the issue of slow inference speed. This will improve the safety of autonomous driving systems and provide feasibility for devices with limited computing power to achieve autonomous ***/methodology/approachBRTPillar first adopts an element-based method to fuse image and point cloud features. Secondly, a local-global feature interaction method based on an efficient additive attention mechanism was designed to extract multi-scale contextual information. Finally, an enhanced multi-scale feature fusion method was proposed by introducing adaptive spatial and channel interaction attention mechanisms, thereby improving the learning of fine-grained *** experiments were conducted on the KITTI dataset. The results showed that compared with the benchmark model, the accuracy of cars, pedestrians and cyclists on the 3d object box improved by 3.05, 9.01 and 22.65%, respectively;the accuracy in the bird's-eye view has increased by 2.98, 10.77 and 21.14%, respectively. Meanwhile, the running speed of BRTPillar can reach 40.27 Hz, meeting the real-time detection needs of autonomous ***/valueThis paper proposes a boosting multimodal real-time 3d object detection method called BRTPillar, which achieves accurate location in many scenarios, especially for complex scenes with many small objects, while also achieving real-time inference speed.

关键词： Autonomous driving Multimodal 3d object detection Attention mechanism

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NRAP-RCNN: A Pseudo Point Cloud 3d object detection Method Based on Noise-Reduction Sparse Convolution and Attention Mechanism

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INFORMATION 2025年第3期16卷 176-176页

作者： Zhou, Ziyue Jia, Yongqing Zhu, Tao Wan, Yaping Univ South China Sch Comp Sci Hengyang 421001 Peoples R China Hunan Elect Res Inst Testing Grp Co Ltd Technol Dept Xiangxiang 411402 Peoples R China

In recent years, pseudo point clouds generated from depth completion of RGB images and LidAR data have provided a robust foundation for multimodal 3d object detection. However, the generation process often introduces noise, reducing data quality and detection accuracy. Moreover, existing methods fail to effectively capture channel correlations and global contextual information during the 2d feature extraction stage after the 3d backbone network, limiting detection performance. To address these challenges, this paper proposes NRAP-RCNN, a pseudo point cloud-based 3d object detection method with two key innovations: (1) A noise-reduction sparse convolution network (NRConvNet), comprising NRConv (noise-resistant submanifold sparse convolution), SRB (sparse convolution residual block), and MHSA (multi-head self-attention). NRConv suppresses pseudo point cloud noise by jointly encoding 2d and 3d features, SRB enhances feature extraction depth and robustness, and MHSA optimizes global feature representation. (2) An attention fusion module (ECA_GCA) is introduced to enhance the feature representation of the 2d backbone network by combining channel and global contextual information. The experimental results demonstrate that NRAP-RCNN achieves 88.4% car AP (R40) on the KITTI validation set and 85.1% on the test set, significantly outperforming advanced 3d detection methods, showcasing its effectiveness in improving detection performance.

关键词： pseudo point cloud 3d object detection noise reduction sparse convolution attention mechanism

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VPC-VoxelNet: multi-modal fusion 3d object detection networks based on virtual point clouds

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INTERNATIONAL JOURNAL OF MULTIMEdIA INFORMATION RETRIEVAL 2025年第1期14卷 1-11页

作者： Zhang, Qiang Shi, Qin Cheng, Teng Zhang, Junning Chen, Jiong Hefei Univ Technol Engn Res Ctr Intelligent Transportat & Cooperat Ve Key Lab Automated Vehicle Safety Technol Anhui Pro Hefei 230009 Peoples R China Chery Automobile Co Ltd Wuhu 241000 Peoples R China Natl Univ Def Technol Sch Elect Countermeasures Hefei 230041 Peoples R China Nio Automot Technol Anhui Co LTD Hefei 230041 Peoples R China

To address the impact of sparsity and disorder of point clouds on object detection accuracy, this paper proposes a multi-modal fusion network VPC-VoxelNet based on virtual point clouds. Firstly, virtual point clouds are constructed using image detection object information to increase the density of point clouds, thus improving the performance of object features;Secondly, increasing the dimensionality of point cloud features, distinguishing virtual point clouds and avoiding the accumulation of multi model errors;Finally, an optimized loss function such as the scale factor of the virtual point cloud is used to improve the training efficiency of the multi-modal network. The object detection network, VPC-VoxelNet, was tested on the KITTI dataset, and the detection accuracy was better than that of the classical 3d point cloud detection network and certain multi-modal information fusion networks, with a vehicle detection accuracy of 86.9%.

关键词： 3d object detection Multi-modal fusion Virtual point cloud Adaptive optimizer

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MSFNet3d: Monocular 3d object detection via dual-Branch depth-Consistent Fusion and Semantic-Guided Point Cloud Refinement

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WORLd ELECTRIC VEHICLE JOURNAL 2025年第3期16卷 173-173页

作者： Yang, Rong You, Zhijie Luo, Renhui Guangxi Univ Coll Mech Engn Nanning 530004 Peoples R China

The rapid development of autonomous driving has underscored the pivotal role of 3d perception. Monocular 3d object detection, as a cost-effective alternative to expensive lidar systems, is garnering increasing attention. However, existing pseudo-lidar methods encounter challenges such as coarse quality and insufficient semantic information when generating 3d point clouds from monocular images. To address these issues, this paper introduces MSFNet3d, which aims to overcome the quality limitations of pseudo-lidar point cloud. Our contributions are threefold: (1) We introduce a dual-branch network to optimize depth maps and propose a multi-scale channel spatial attention module (MS_CBAM). This module captures multi-scale geometric features through a hierarchical feature pyramid and an adaptive weight allocation mechanism, thereby addressing the scale sensitivity inherent in traditional attention mechanisms. (2) We propose a consistency-weighted fusion strategy that employs local gradient consistency analysis and differentiable weighted optimization to achieve a pixel-level fusion of image and depth features. This approach reduces feature conflicts within the dual-branch network and enhances the model's robustness in complex scenes. (3) We introduce a semantic-guided pseudo-point cloud enhancement method that leverages an instance segmentation network to extract object-specific semantic regions and generate high-confidence point cloud, consequently improving the accuracy of object detection. Experiments on the KITTI dataset show that the proposed method performs excellently under various detection challenges, achieving an average precision of 18.87% in the 3d detection of car objects, which is a 1.67% improvement over the original model. The method also shows good performance in detecting pedestrians and cyclists. The proposed framework can provide economical and reliable 3d perception for mass-produced electric vehicles.

关键词： autonomous driving 3d object detection pseudo-point cloud attention mechanism

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Alternating interaction fusion of Image-Point cloud for Multi-Modal 3d object detection

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AdVANCEd ENGINEERING INFORMATICS 2025年 65卷

作者： Li, Guofa Lu, Haifeng Li, Jie Li, Zhenning Li, Qingkun Ren, Xiangyun Zheng, Ling Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Shenzhen Univ Inst Human Factors & Ergon Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Chinese Acad Sci Inst Software Beijing Key Lab Human Comp Interact Beijing 100190 Peoples R China Chongqing Changan Automobile Co Ltd State Key Lab Intelligent Vehicle Safety Technol Chongqing 400023 Peoples R China

A mainstream feature fusion method involves enhancing Lidar point cloud information by incorporating camera, but it fails to fully utilize the rich information in images. Another method uses a dual-channel parallel approach to fuse image and point cloud information, but it also faces issues such as excessive module stacking and high computational demands. Therefore, we propose a powerful alternating interaction fusion approach. Firstly, it resolves the problem of unilateral fusion schemes that overly rely on point cloud information and fail to fully utilize image data. Secondly, it tackles the problem of excessive module stacking and high computational demands in dual-channel parallel fusion schemes of point cloud and image data. Specifically, our alternate interactive fusion module implements a method where image and point cloud BEV features mutually enhance each other. Local attention interactions are engaged between image features containing point cloud information and regular image features. This enhances the expressiveness of image features. Subsequently, internal BEV attention interactions occur between point cloud BEV features with enriched image information and regular point cloud BEV features. This step improves the expressiveness of the point cloud BEV features. Experiments on the largescale nuScenes dataset demonstrate that our proposed method outperforms both the unilateral point cloudcentric fusion and the parallel interactive fusion approaches.

关键词： 3d object detection Camera-LidAR fusion Autonomous Vehicles

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Weakly Supervised 3d object detection via Multi-level Visual Guidance 18th

Weakly Supervised 3D Object Detection via Multi-level Visual...

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18th European Conference on Computer Vision (ECCV)

作者： Huang, Kuan-Chih Tsai, Yi-Hsuan Yang, Ming-Hsuan Univ Calif Merced CA 95343 USA Google Mountain View CA USA

ISBN: (纸本)9783031732317;9783031732324

Weakly supervised 3d object detection aims to learn a 3d detector with lower annotation cost, e.g., 2d labels. Unlike prior work which still relies on few accurate 3d annotations, we propose a framework to study how to leverage constraints between 2d and 3d domains without requiring any 3d labels. Specifically, we employ visual data from three perspectives to establish connections between 2d and 3d domains. First, we design a feature-level constraint to align LidAR and image features based on object-aware regions. Second, the output-level constraint is developed to enforce the overlap between 2d and projected 3d box estimations. Finally, the training-level constraint is utilized by producing accurate and consistent 3d pseudo-labels that align with the visual data. We conduct extensive experiments on the KITTI dataset to validate the effectiveness of the proposed three constraints. Without using any 3d labels, our method achieves favorable performance against state-of-the-art approaches and is competitive with the method that uses 500-frame 3d annotations. Code and models will be made publicly available.

关键词： 3d object detection Weakly-Supervised 3d object detection Visual Guidance

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ASPVNet: Attention Based Sparse Point-Voxel Network for 3d object detection 7th

ASPVNet: Attention Based Sparse Point-Voxel Network for 3D O...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Yu, Bingxin Wang, Lu He, Yuhong Wang, Xiaoyang Cheng, Jun Northeastern Univ Shenyang Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9789819787913;9789819787920

As the core perception component, 3d object detection plays a crucial role in autonomous driving and robot navigation systems. However, most existing point-voxel based methods rely on feature conversion and complex fusion or feature aggregation modules based on proposals, which require significant computational overhead. In this paper, we propose an attention based sparse point-voxel network (ASPVNet), which is an efficient point-voxel based approach that extracts features consecutively using point and voxel representations and leverages their respective advantages. Specifically, an attention based sparse point-voxel convolution is introduced that extracts point features using channel attention and the resulting features are used to update the corresponding voxel features, followed by sparse convolution for extracting voxel features. We also propose an auxiliary segmentation module based on 3d heatmap that generates 3d heatmap soft labels to emphasize the foreground points and the overlooked background points. By incorporating category, geometry, and structural information into soft labels can also enhance the network's ability to perceive structures. In addition, we present a data augmentation method to ease the point cloud density imbalance problem, which uses nearby instances to simulate distant sparse instances to improve the detection accuracy of distant instances. Experiments on the KITTI dataset demonstrate that our method plays favorably against existing state-of-the-art methods.

关键词： Point-voxel convolution 3d Gaussion heatmap 3d object detection Autonomous driving

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LiFT: Lightweight, FPGA-Tailored 3d object detection Based on LidAR data 18th

LiFT: Lightweight, FPGA-Tailored 3D Object Detection Based o...

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18th International Workshop on design and Architecture for Signal and Image Processing, dASIP 2025

作者： Lis, Konrad Kryjak, Tomasz Gorgoń, Marek Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031878961

This paper presents LiFT, a lightweight, fully quantized 3d object detection algorithm for LidAR data, optimized for real-time inference on FPGA platforms. Through an in-depth analysis of FPGA-specific limitations, we identify a set of FPGA-induced constraints that shape the algorithm’s design. These include a computational complexity limit of 30 GMACs (billion multiply-accumulate operations), INT8 quantization for weights and activations, 2d cell-based processing instead of 3d voxels, and minimal use of skip connections. To meet these constraints while maximizing performance, LiFT combines novel mechanisms with state-of-the-art techniques such as reparameterizable convolutions and fully sparse architecture. Key innovations include the dual-bound Pillar Feature Net, which boosts performance without increasing complexity, and an efficient scheme for INT8 quantization of input features. With a computational cost of just 20.73 GMACs, LiFT stands out as one of the few algorithms targeting minimal-complexity 3d object detection. Among comparable methods, LiFT ranks first, achieving an mAP of 51.84% and an NdS of 61.01% on the challenging NuScenes validation dataset. The code will be available at https://***/vision-agh/lift. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3d object detection FPGA LidAR NuScenes quantisation

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MutualForce: Mutual-Aware Enhancement for 4d Radar-LidAR 3d object detection

MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Peng, Xiangyuan Sun, Huawei Bierzynski, Kay Fischbacher, Anton Servadei, Lorenzo Wille, Robert Technical University of Munich Munich Germany Infineon Technologies AG Neubiberg Germany

ISBN: (纸本)9798350368741

Radar and LidAR have been widely used in autonomous driving as LidAR provides rich structure information, and radar demonstrates high robustness under adverse weather. Recent studies highlight the effectiveness of fusing radar and LidAR point clouds. However, challenges remain due to the modality misalignment and information loss during feature extractions. To address these issues, we propose a 4d radar-LidAR framework to mutually enhance their representations. Initially, the indicative features from radar are utilized to guide both radar and LidAR geometric feature learning. Subsequently, to mitigate their sparsity gap, the shape information from LidAR is used to enrich radar BEV features. Extensive experiments on the View-of-delft (Vod) dataset demonstrate our approach's superiority over existing methods, achieving the highest mAP of 71.76% across the entire area and 86.36% within the driving corridor. Especially for cars, we improve the AP by 4.17% and 4.20% due to the strong indicative features and symmetric shapes. © 2025 IEEE.

关键词： 3d object detection Autonomous driving Indicative feature Sensor fusion Shape information

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RAFdet: A Novel Camera-Radar Fusion Framework for Robust 3d object detection in Autonomous driving

RAFDet: A Novel Camera-Radar Fusion Framework for Robust 3D ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cao, Xingjian Wang, Ping Zhang, Zhitao Tu, Huizhao Chen, Yong Liang, Zhenbao College of Electronic and Information Engineering Tongji University Shanghai China College of Transportation Tongji University Shanghai China Co. Ltd. Ningbo China

ISBN: (纸本)9798350368741

Accurate and reliable 3d object detection is crucial for autonomous driving, normally achieved using camera-only or camera-LidAR fusion methods based on BEV (Bird's Eye View) perspective. However, visual perception through cameras alone faces significant challenges, such as ambiguous depth estimation and poor performance in low-light, while camera-LidAR fusion, though robust, are expensive on deployment and have limitations in dust and fog weather conditions. To address these issues, we propose RAFdet (Radar-Assisted Fusion detection), a novel multi-modality fusion framework integrating camera and radar data for enhanced 3d detection. RAFdet utilizes radar RF (Radio Frequency) images to enrich spatial details, boosting fusion effects, while radar point clouds provide precise depth information to rectify visual depth predictions. In addition, the Edge-Awareness Feature Enhancement mechanism compensates for the sparsity of radar points, further refining depth estimation and detection accuracy. Our extensive experiments demonstrate the superiority of RAFdet over current methods, highlighting its potential for autonomous driving systems. Key results show significant improvements in depth prediction and overall performance metrics, validating the effectiveness of our approach. Our data collection and testing in real traffic scenarios also reflects the robustness, accuracy, and cost-effectiveness of RAFdet for 3d object detection in autonomous driving. © 2025 IEEE.

关键词： 3d object detection Autonomous driving Camera-radar fusion depth-rectification view transformation Edge-awareness feature enhancement Radar RF image

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