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Point-Level Fusion and Channel Attention for 3d object detection in Autonomous driving

SENSORS 2025年第4期25卷 1097-1097页

作者： Shen, Juntao Fang, Zheng Huang, Jin Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China Sichuan Inst Land Sci & Technol Sichuan Ctr Satellite Applicat Technol Chengdu 610072 Peoples R China

As autonomous driving technology progresses, LidAR-based 3d object detection has emerged as a fundamental element of environmental perception systems. PointPillars transforms point cloud data into a two-dimensional pseudo-image and employs a 2d CNN for efficient and precise detection. Nevertheless, this approach encounters two primary challenges: (1) the sparsity and disorganization of raw point clouds hinder the model's capacity to capture local features, thus impacting detection accuracy;and (2) existing models struggle to detect small objects within complex environments, particularly regarding orientation estimation. To address these issues, we propose two enhancements: (1) point-level fusion of LidAR point clouds and RGB images, which integrates the semantic information of 2d images with the geometric features of 3d point clouds to improve model performance in intricate scenarios;(2) the incorporation of the Efficient Channel Attention mechanism to concentrate on essential features, particularly for small and sparse objects. Experimental results on the KITTI dataset indicate significant improvements, particularly in small object detection tasks, such as identifying pedestrians and cyclists. The enhanced model also demonstrates substantial gains in the Average Orientation Similarity (AOS) metric. These enhancements enhance the vehicle's ability to track and predict object trajectories in dynamic environments, critical for reliable recognition and decision-making.

关键词： autonomous driving 3d object detection multimodal fusion channel attention mechanism ECA

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dSC3d: deformable Sampling Constraints in Stereo 3d object detection for Autonomous driving

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IEEE TRANSACTIONS ON CIRCUITS ANd SYSTEMS FOR VIdEO TECHNOLOGY 2025年第3期35卷 2794-2805页

作者： Chen, Jiawei Song, Qi Guo, Wenzhong Huang, Rui Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China Chinese Univ Hong Kong Shenzhen CUHKSZ Sch Sci & Engn Hong Kong 518172 Guangdong Peoples R China

Camera-based stereo 3d object detection estimates 3d properties of objects with binocular images only, which is a cost-effective solution for autonomous driving. The state-of-the-art methods mainly improve the detection accuracy of general objects by designing ingenious stereo matching algorithms or complex pipeline modules. Moreover, additional fine-grained annotations, such as masks or LidAR point clouds, are often introduced to deal with the occlusion problems, which brings in high manual costs for this task. To address the detection bottleneck caused by occlusion in a more cost-effective manner, we develop a novel stereo 3d object detection method named dSC3d, which achieves significant improvements for occluded objects without introducing additional supervision. Specifically, we first report the ambiguity in feature sampling, which refers to the presence of noisy features in the sampling for occluded objects. Then, we propose the Epipolar Constraint deform-Attention (ECdA) module to address the unreliable left-right correspondence computation in stereo matching caused by occlusion, which reweights epipolar features by adaptively aggregating local neighbor information. Furthermore, to ensure that 3d property estimation is based on robust object features, we propose visible regions guided constraint to explicitly guide the offset learning for feature sampling. Extensive experiments conducted on the KITTI benchmark have demonstrated the proposed dSC3d outperforms the state-of-the-art camera-based methods.

关键词： 3d object detection stereo matching binocular images binocular images occluded object occluded object autonomous driving autonomous driving autonomous driving

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MIM: High-definition Maps Incorporated Multi-View 3d object detection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 3989-4001页

作者： Xiao, Jinsheng Wang, Shurui Zhou, Jian Tian, Ziyue Zhang, Hongping Wang, Yuan-Fang Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430072 Peoples R China Wuhan Univ GNSS Res Ctr Wuhan 430072 Peoples R China Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA

3d object detection has aroused increasing interest as a crucial component of autonomous driving systems. While recent works have explored various multi-modal fusion methods to enhance accuracy and robustness, fusing multi-view images and high-definition (Hd) maps remains uncharted. Inspired by our previous work, we endeavor to introduce Hd maps to camera-based detection, prompting the design of a new framework. To address this, we first analyze the function of Hd maps in object detection to understand their benefits and the rationale for their fusion. From this analysis, we identify key disparities in view, semantics, and scale, leading to the development of MIM, a framework for Hd Maps Incorporated Multi-view 3d object detection. Hd maps are enriched in semantics by sampling unlabeled areas and encoding them into map features. Simultaneously, multi-view images are transformed into features in bird's-eye view (BEV) using the adopted baseline. These features are then fused using attention mechanisms to align scales. Experiments conducted on the nuScenes dataset demonstrate that MIM outperforms camera-based methods. Moreover, an in-depth analysis investigates how Hd maps impact object detection regarding each semantic layer. The results underscore the operational intricacies of Hd maps in perception, setting the stage for future research. Code is available at https://***/WHU-xjs/MIM-3d-det.

关键词： 3d object detection autonomous driving multi- modal fusion multi- modal fusion multi-view images high-definition maps high-definition maps high-definition maps

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Bi-Att3ddet: Attention-Based Bi-directional Fusion for Multi-Modal 3d object detection

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SENSORS 2025年第3期25卷 658-658页

作者： Gao, Xu Zhao, Yaqian Wang, Yanan Shang, Jiandong Zhang, Chunmin Wu, Gang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Natl Supercomp Ctr Zhengzhou Zhengzhou 450001 Peoples R China Yutong Bus Co Ltd Zhengzhou 450000 Peoples R China

Currently, multi-modal 3d object detection methods have become a key area of research in the field of autonomous driving. Fusion is an essential factor affecting performance in multi-modal object detection. However, previous methods still suffer from the inability to effectively fuse features from LidAR and RGB images, resulting in a low utilization rate of complementary information between depth and semantic texture features. At the same time, existing methods may not adequately capture the structural information in Region of Interest (RoI) features when extracting them. Structural information plays a crucial role in RoI features. It encompasses the position, size, and orientation of objects, as well as the relative positions and spatial relationships between objects. Its absence can result in false or missed detections. To solve the above problems, we propose a multi-modal sensor fusion network, Bi-Att3ddet, which mainly consists of a Self-Attentive RoI Feature Extraction module (SARoIFE) and a Feature Bidirectional Interactive Fusion module (FBIF). Specifically, SARoIFE captures the relationship between different positions in RoI features to obtain high-quality RoI features through the self-attention mechanism. SARoIFE prepares for the fusion stage. FBIF performs bidirectional interaction between LidAR and pseudo RoI features to make full use of the complementary information. We perform comprehensive experiments on the KITTI dataset, and our method notably demonstrates a 1.55% improvement in the hard difficulty level and a 0.19% improvement in the mean Average Precision (mAP) metric on the test dataset.

关键词： multi-modal sensor fusion attention mechanism 3d object detection autonomous driving

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PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3d object detection

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 281卷

作者： Li, Yidi Wen, Jiahao Gong, Rui Ren, Bin Li, Wenhao Cheng, Chen Liu, Hong Sebe, Nicu Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan Peoples R China Peking Univ Shenzhen Grad Sch Key Lab Machine Percept Shenzhen Peoples R China Univ Pisa Pisa Italy Univ Trento Trento Italy

The integration of point and voxel representations is becoming more common in Light detection and Ranging (LidAR)-based 3d object detection. However, existing fusion strategies suffer from ineffective semantic alignment and contextual information loss, while relying solely on point features within regions of interest leads to geometric detail degradation and limited local-global feature integration. To tackle these challenges, we propose the Point-Voxel Attention Fusion Network (PVAFN), a novel two-stage 3d object detector that introduces a point-voxel attention fusion module based on dual-gated cross-modal interaction and a multi-pooling strategy based on density-space awareness. during the feature extraction and fusion stage, a dual-gated hierarchical attention mechanism is proposed to dynamically fuse three heterogeneous modalities-keypointbased geometric details, voxel-wise local regularity, and Bird's-Eye-View (BEV)-level global semantics-through learnable gating functions. In the refinement stage, a density-spatial-aware multi-pooling enhancement module is designed to synergize density-aware cluster pooling and multi-scale spatial-aware pyramid pooling, efficiently capturing key geometric details and fine-grained shape structures. This design enhances the integration of local and global features while enabling adaptive multi-scale context modeling and spatially sensitive feature aggregation. Extensive experiments on the KITTI and Waymo benchmark datasets demonstrate that PVAFN achieves promising detection accuracy in 3d mean Average Precision.

关键词： 3d object detection Point-voxel fusion Attention module Pooling enhancement

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PRNet: 3d object detection Network-Based on Point-Region Fusion

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APPLIEd SCIENCES-BASEL 2025年第7期15卷 3759-3759页

作者： Fu, Yufei Guo, Yuhao Hu, Hui Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England East China Jiaotong Univ Coll Informat Engn Nanchang 330013 Peoples R China

object detection is a pivotal task in the realm of autonomous driving, where reliance on single-modality information often proves inadequate for high-precision detection tasks. In current research, object detection networks based on point clouds effectively identify objects with dense point clouds. However, these networks face challenges when dealing with detection tasks involving objects with higher point cloud sparsity. To address this issue, this paper proposes a 3d object detection network based on point cloud and image fusion. This network employs a fusion module named PRF (Point-Region Fusion), utilizing the K-Nearest Neighbors (KNN) algorithm to find the nearest K points corresponding to point cloud features. It then collects regional features from image feature maps and fuses them with the point cloud features after aggregation. The designed Image Feature Fusion module (IF-Fusion) fuses image feature maps of varying sizes in a pairwise manner. This fusion process is key in preserving features of small objects and subsequently enriching the point cloud's features. In evaluations conducted on the KITTI benchmark, the method presented here surpasses prior fusion networks in detection accuracy, achieving rates of 91.95%, 81.10%, and 78.08%.

关键词： 3d object detection Point-Region Fusion K-Nearest Neighbors KITTI

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Boosting 3d object detection via Self-distilling Introspective data

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 6587-6600页

作者： Wang, Chaoqun Qin, Yiran Kang, Zijian Ma, Ningning Shi, Yukai Li, Zhen Zhang, Ruimao Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China NIO Autonomous Driving Div Beijing 100190 Peoples R China Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Chinese Univ Hong Kong Shenzhen 518172 Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China

3d object detection is a fundamental yet critical task for autonomous driving. In this paper, we investigate a novel self-distilling paradigm by proposing Self-distilling Introspective data (SId) to boost the accuracy of 3d object detection in both LidAR-based and LidAR-Camera-based scenarios. The proposed SId significantly improves the applicability of the distillation approach since it does not require extra training data or complex teacher network design. Specifically, we first employ an introspective data augmentation method to enrich object-aware information in sparse point clouds through geometric or semantic injection. We then utilize this enhanced data to train a robust teacher model. In contrast to traditional distillation that relies on larger models to enhance the representations of smaller ones, the teacher model in SId shares the same architecture as the student model but exhibits exceptionally high discriminative ability. This enables the effective transfer of rich feature representations to the student model. Rooted on such a scheme, when conducting LidAR-based detectors, SId significantly enhances the semantic representation capabilities of sparse point clouds. Additionally, in the LidAR-Camera-based setting, SId also effectively supervises the fusion of the two modalities at the feature level, ensuring more reasonable cross-modal learning. Extensive experiments show the proposed SId improves a variety of detectors. For the LidAR-based detector, the SId gains 2.31% mAP improvements for the hard objects in KITTI, while 1.76% NdS improvements on nuScenes. For the LidAR-Camera-based detectors, the SId boosts the detection accuracy significantly, with 1.5% mAP promotion on KITTI and 2.15% NdS improvements on the nuScenes benchmark.

关键词： Three-dimensional displays Laser radar Feature extraction detectors object detection data models Solid modeling Semantics Point cloud compression Training Knowledge distillation 3d object detection LidAR-camera fusion introspective data augmentation

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Multi-Modal Fusion Based on depth Adaptive Mechanism for 3d object detection

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IEEE TRANSACTIONS ON MULTIMEdIA 2025年 27卷 707-717页

作者： Liu, Zhanwen Cheng, Juanru Fan, Jin Lin, Shan Wang, Yang Zhao, Xiangmo Changan Univ Sch Informat Engn Xian 710064 Peoples R China Univ Sci & Technol China Sch Informat Hefei 230026 Peoples R China

Lidars and cameras are critical sensors for 3d object detection in autonomous driving. despite the increasing popularity of sensor fusion in this field, accurate and robust fusion methods are still under exploration due to non-homogenous representations. In this paper, we find that the complementary roles of point clouds and images vary with depth. An important reason is that the point cloud appearance changes significantly with increasing distance from the Lidar, while the image's edge, color, and texture information are not sensitive to depth. To address this, we propose a fusion module based on the depth Attention Mechanism (dAM), which mainly consists of two operations: gated feature generation and point cloud division. The former adaptively learns the importance of bimodal features without additional annotations, while the latter divides point clouds to achieve differential fusion of multi-modal features at different depths. This fusion module can enhance the representation ability of original features for different point sets and provide more comprehensive features by using the dual splicing strategy of concatenation and index connection. Additionally, considering point density as a feature and its negative correlation with depth, we build an Adaptive Threshold Generation Network (ATGN) to generate the depth threshold by extracting density information, which can divide point clouds more reasonably. Experiments on the KITTI dataset demonstrate the effectiveness and competitiveness of our proposed models.

关键词： Point cloud compression Three-dimensional displays Feature extraction object detection Laser radar Image color analysis detectors deep learning 3d object detection sensor fusion Lidar sensor camera sensor

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dART3d: depth-Aware Robust Adversarial Training for Monocular 3d object detection

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ELECTRONICS LETTERS 2025年第1期61卷

作者： Ju, Xinrui Shang, Xiaoke Li, Xingyuan Ren, Bohua Dongbei Univ Finance & Econ Dalian Peoples R China Dalian Univ Technol Dalian Peoples R China

Monocular 3d object detection plays a pivotal role in the field of autonomous driving and numerous deep learning-based methods have made significant breakthroughs in this area. despite the advancements in detection accuracy and efficiency, these models tend to fail when faced with adversarial attacks, rendering them ineffective. Therefore, bolstering the adversarial robustness of 3d detection models has become a critical issue. To mitigate this issue, we propose a depth-aware robust adversarial training method for monocular 3d object detection, dubbed dART3d. Specifically, we first design an adversarial attack that iteratively degrades the 2d and 3d perception capabilities of 3d object detection models (iterative deterioration of perception), serving as the foundation for our subsequent defense mechanism. In response to this attack, we propose an uncertainty-based residual learning method for adversarial training. Our adversarial training leverages inherent uncertainty to boost robustness against attacks while incorporating depth-aware information enhances resistance to perturbations in both 2d and 3d domains. We conducted extensive experiments on the KITTI 3d dataset, showing that dART3d outperforms direct adversarial training in 3d object detection APR(40) for the car category, with improvements of 4.415%, 4.112% and 3.195% in easy, moderate and hard settings, respectively.

关键词： Adversarial attack 3d object detection monocular vision

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dispersion Adaptive Convolution: Robust Multi-Modal 3d object detection by Incorporating Sensor Characteristics

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JOURNAL OF CIRCUITS SYSTEMS ANd COMPUTERS 2025年第7期34卷

作者： Chen, Yaqing Wang, Huaming Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China

Multi-modal three-dimensional (3d) roadside object detection is a challenging yet critical topic for Vehicle-Infrastructure Cooperated Autonomous driving (VICAd). Recently, the Birds-Eye View (BEV) framework has emerged as a promising solution. However, current approaches that process BEV features with isotropic convolutions fail to account for the error characteristics of sensors, leading to inefficiency and instability in disturbed scenarios. This paper addresses this issue by developing the Sensor Characteristics Adaptive Bird's Eye View (SCA-BEV), a simple yet effective roadside 3d object detection framework based on improved deformable convolution and anisotropic attention mechanism. Specifically, we designed the error dispersion Adaptive Convolution (dAC) to enhance the network's anti-interference ability. Moreover, a novel axial attention mechanism was proposed to improve multi-modality fusion. Additionally, we introduce a knowledge distillation framework to boost the performance in camera-only scenarios. The proposed method was validated on the dAIR-V2X dataset and compared against state-of-the-art networks. The experimental results demonstrate that our method achieves higher detection accuracy with remarkable real-time performance in disturbed scenarios, suggesting a broad application prospect in intelligent roadside systems.

关键词： ITS 3d object detection deformable convolutional axial attention

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