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

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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PFENet: Towards precise feature extraction from sparse point cloud for 3d object detection

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NEURAL NETWORKS 2025年 185卷 107144页

作者： Li, Yaochen Li, Qiao Gao, Cong Gao, Shengjing Wu, Hao Liu, Rui Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China

Accurate 3d point cloud object detection is crucially important for autonomous driving vehicles. The sparsity of point clouds in 3d scenes, especially for smaller targets like pedestrians and bicycles that contain fewer points, makes detection particularly challenging. To solve this problem, we propose a single-stage voxel-based 3d object detection method, namely PFENet. Firstly, we design a robust voxel feature encoding network that incorporates a stacked triple attention mechanism to enhance the extraction of key features and suppress noise. Moreover, a 3d sparse convolution layer dynamically adjusts feature processing based on output location importance, improving small object recognition. Additionally, the attentional feature fusion module in the region proposal network merges low-level spatial features with high-level semantic features, and broadens the receptive field through atrous spatial pyramid pooling to capture multi-scale features. Finally, we develop multiple detection heads for more refined feature extraction and object classification, as well as more accurate bounding box regression. Experimental results on the KITTI dataset demonstrate the effectiveness of the proposed method.

关键词： 3d object detection Sparse point cloud Autonomous driving

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Adversarial Obstacle Generation Against LidAR-Based 3d object detection

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IEEE TRANSACTIONS ON MULTIMEdIA 2024年 26卷 2686-2699页

作者： Wang, Jian Li, Fan Zhang, Xuchong Sun, Hongbin Xi An Jiao Tong Univ Sch Informat & Commun Engn Shaanxi Key Lab Deep Space Explorat Intelligent In Xian 710049 Peoples R China Xi An Jiao Tong Univ Coll Artificial Intelligence Xian 710049 Peoples R China

LidAR sensors are widely used in many safety-critical applications such as autonomous driving and drone control, and the collected data called point clouds are subsequently processed by 3d object detectors for visual perception. Recent works have shown that attackers can inject virtual points into LidAR sensors by strategically transmitting laser pulses to them;additionally, deep visual models have been found to be vulnerable to carefully crafted adversarial examples. Therefore, a LidAR-based perception may be maliciously attacked with serious safety consequences. In this article, we present a highly-deceptive adversarial obstacle generation algorithm against deep 3d detection models, to mimic fake obstacles within the effective detection range of LidAR using a limited number of points. To achieve this goal, we first perform a physical LidAR simulation to construct sparse obstacle point clouds. Then, we devise a strong attack strategy to adversarially perturb prototype points along each direction of the ray. Our method achieves a high attack success rate while complying with physical laws at the hardware level. We perform comprehensive experiments on different types of 3d detectors and determine that the voxel-based detectors are more vulnerable to adversarial attacks than the point-based methods. For example, our approach achieves an 89% mean attack success rate against PV-RCNN by using only 20 points to spoof a fake car.

关键词： Three-dimensional displays Laser radar Point cloud compression Sensors detectors Perturbation methods Solid modeling Adversarial attack LidAR simulation 3d object detection point cloud perturbation

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Reinforced Voxel-RCNN: An Efficient 3d object detection Method Based on Feature Aggregation*

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IEICE TRANSACTIONS ON INFORMATION ANd SYSTEMS 2024年第9期E107D卷 1228-1238页

作者： Jiang, Jia-ji Wan, Hai-bin Sun, Hong-min Qin, Tuan-fa Wang, Zheng-qiang Guangxi Univ Sch Comp Elect & Informat Nanning Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun & Network Techno Nanning Peoples R China Guangxi Vocat & Tech Inst Ind Nanning 530001 Guangxi Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

In this paper, the Towards High Performance Voxel-based object detection model is used as the benchmark network. Aiming at the problems existing in the current mainstream 3d point cloud voxelization methods, such as the backbone and the lack of feature expression ability under the bird's-eye view (BEV), a high-performance voxel-based 3d object detection network (Reinforced Voxel-RCNN) is proposed. Firstly, a 3d feature extraction module based on the integration of inverted residual convolutional network and weight normalization is designed on the 3d backbone. This module can not only well retain more point cloud feature information, enhance the information interaction between convolutional layers, but also improve the feature extraction ability of the backbone network. Secondly, a spatial feature-semantic fusion module based on spatial and channel attention is proposed from a BEV perspective. The mixed use of channel features and semantic features further improves the network's ability to express point cloud features. In the comparison of experimental results on the public dataset KITTI, the experimental results of this paper are better than many voxel-based methods. Compared with the baseline network, the 3d average accuracy and BEV average accuracy on the three categories of Car, Cyclist, and Pedestrians are improved. Among them, in the 3d average accuracy, the improvement rate of Car category is 0.23%, Cyclist is 0.78%, and Pedestrians is 2.08%. In the context of BEV average accuracy, enhancements are observed: 0.32% for the Car category, 0.99% for Cyclist, and 2.38% for Pedestrians. The findings demonstrate that the algorithm enhancement introduced in this study effectively enhances the accuracy of target category detection.

关键词： 3d object detection inverted residual sparse convolution spatial semantic feature fusion weight normalization

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

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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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Exploiting Label Uncertainty for Enhanced 3d object detection From Point Clouds

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第6期25卷 6074-6089页

作者： Sun, Yang Lu, Bin Liu, Yonghuai Yang, Zhenyu Behera, Ardhendu Song, Ran Yuan, Hejin Jiang, Haiyan North China Elect Power Univ Engn Res Ctr Intelligent Comp Complex Energy Syst Minist Educ Baoding 071003 Peoples R China Edge Hill Univ Intelligent Visual Comp Res Ctr Ormskirk L39 4QP Lancs England Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China Nanjing Agr Univ Coll Artificial Intelligence Nanjing 210095 Peoples R China

Accurate detection of objects from LidAR point clouds is crucial for autonomous driving and environment modeling. However, uncertainties in ground truth labels due to occlusions, sparsity, and truncation can hinder model training and performance. This paper introduces two strategies to address these issues: 1) Soft Regression Loss (SoRL) and 2) discrete Quantization Sampling (dQS). SoRL utilizes Gaussian distributions for object predictions, measuring uncertainty based on the probability of ground truth labels within these distributions. This method effectively accounts for deviations in object location and orientation. Meanwhile, dQS introduces uncertainty scores for dynamic sample selection, aiming to refine the quality of positive samples for regression. Based on the proposed modules, we design a lightweight multi-stage object detection framework. Notably, these modules can enhance existing 3d object detection methods without affecting significantly inference speeds. Experiments over benchmark datasets show the effectiveness of our method, especially for cars in sparse point clouds.

关键词： 3d object detection deep learning point clouds soft regression loss dynamic sample selection

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ARIoU: Anchor-free Rotation-decoupling IoU-based optimization for 3d object detection

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NEUROCOMPUTING 2024年 594卷

作者： Wen, Chenyiming Sheng, Hualian Zhao, Ming-Min Zhao, Min-Jian Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China

The rotation-decoupling strategy was developed in outdoor 3d object detection with certain performance improvement. However, its anchor-based architecture limits its further improvement in indoor 3d object detection. In this study, we propose an Anchor-free Rotation-decoupling IoU-based optimization, termed ARIoU, which is specifically devised to indoor 3d object detection. A pivotal aspect of our approach is a customized encoding-decoding scheme that integrates the anchor-free framework with the rotation decoupling strategy to mitigate the negative effect caused by rotation sensitivity. Specifically, the unwanted length/width/rotation prediction randomness induced by labeled rotation ambiguity is alleviated. Furthermore, we plug our ARIoU as the optimization targets in both classification and regression branches to solve the misalignment issue in predicting precision and confidence estimation. Experiments on the SUN RGB-d and S3dIS datasets demonstrate the effectiveness of our approach. Code is available at https://***/wenchened/ARIOU.

关键词： 3d object detection Point cloud Anchor-free detector

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SimLOG: Simultaneous Local-Global Feature Learning for 3d object detection in Indoor Point Clouds

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第12期25卷 19482-19495页

作者： Wei, Mingqiang Chen, Baian Nan, Liangliang Xie, Haoran Gu, Lipeng Lu, dening Wang, Fu Lee Li, Qing Nanjing Univ Aeronaut & Astronaut Sch Comp Sci & Technol Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Shenzhen Inst Res Shenzhen 518000 Peoples R China Delft Univ Technol Urban Data Sci Sect NL-2628 CD Delft Netherlands Lingnan Univ Dept Comp & Decis Sci Hong Kong Peoples R China Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

The acquisition of both local and global features from irregular point clouds is crucial for 3d object detection (3dOd). Current mainstream 3d detectors neglect significant local features during pooling operations or disregard many global features of the overall scene context. This paper proposes new techniques for simultaneously learning local-global features of scene point clouds to enhance 3dOd. Specifically, we propose an efficient 3dOd network in indoor point clouds, named SimLOG, which utilizes simultaneous local-global feature learning. SimLOG has two main contributions: a dynamic Points Interaction (dPI) module to recover local features lost during pooling, and a Global Context Aggregation(GCA) module to aggregate multi-scale features from various layers of the encoder to improve scene context awareness. Unlike traditional local-global feature learning methods, our dPI and GCA modules are integrated into a single feature learning module, making it easily detachable and able to be incorporated into existing 3dOd networks to enhance their performance. SimLOG demonstrates superior performance over twenty competitors in terms of detection accuracy and robustness on both the SUN RGB-d and ScanNet V2 datasets. Specifically, SimLOG boosts the baseline VoteNet by 8.1% of mAP@0.25 on ScanNet V2 and by 3.9% of mAP@0.25 on SUN RGB-d. Code is publicly available at https://***/chenbaian-cs/SimLOG.

关键词： SimLOG 3d object detection dynamic points interaction global context aggregation

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dMFF: dual-way multimodal feature fusion for 3d object detection

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SIGNAL IMAGE ANd VIdEO PROCESSING 2024年第1期18卷 455-463页

作者： dong, Xiaopeng di, Xiaoguang Wang, Wenzhuang Harbin Inst Technol Control & Simulat Ctr Harbin Peoples R China

Recently, multimodal 3d object detection that fuses the complementary information from LidAR data and RGB images has been an active research topic. However, it is not trivial to fuse images and point clouds because of different representations of them. Inadequate feature fusion also brings bad effects on detection performance. We convert images into pseudo point clouds by using a depth completion and utilize a more efficient feature fusion method to address the problems. In this paper, we propose a dual-way multimodal feature fusion network (dMFF) for 3d object detection. Specifically, we first use a dual stream feature extraction module (dSFE) to generate homogeneous LidAR and pseudo region of interest (RoI) features. Then, we propose a dual-way feature interaction method (dWFI) that enables intermodal and intramodal interaction of the two features. Next, we design a local attention feature fusion module (LAFF) to select which features of the input are more likely to contribute to the desired output. In addition, the proposed dMFF achieves the state-of-the-art performances on the KITTI dataset.

关键词： 3d object detection Multimodal feature fusion Self-attention mechanism Lidar point clouds RGB images

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Multi-Sensor Fusion Technology for 3d object detection in Autonomous driving: A Review

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第2期25卷 1148-1165页

作者： Wang, Xuan Li, Kaiqiang Chehri, Abdellah Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China Royal Mil Coll Canada Dept Math & Comp Sci Kingston ON K7K 7B4 Canada

With the development of society, technological progress, and new needs, autonomous driving has become a trendy topic in smart cities. due to technological limitations, autonomous driving is used mainly in limited and low-speed scenarios such as logistics and distribution, shared transport, unmanned retail, and other systems. On the other hand, the natural driving environment is complicated and unpredictable. As a result, to achieve all-weather and robust autonomous driving, the vehicle must precisely understand its environment. The self-driving cars are outfitted with a plethora of sensors to detect their environment. In order to provide researchers with a better understanding of the technical solutions for multi-sensor fusion, this paper provides a comprehensive review of multi-sensor fusion 3d object detection networks according to the fusion location, focusing on the most popular LidAR and cameras currently in use. Furthermore, we describe the popular datasets and assessment metrics used for 3d object detection, as well as the problems and future prospects of 3d object detection in autonomous driving.

关键词： Autonomous driving smart cities multi-sensor fusion 3d object detection LidAR

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