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VirPNet: A Multimodal Virtual Point Generation Network for 3d object detection

IEEE TRANSACTIONS ON MULTIMEdIA 2024年 26卷 10597-10609页

作者： Wang, Lin Sun, Shiliang Zhao, Jing East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

LidAR and camera are the most common used sensors to percept the road scenes in autonomous driving. Current methods tried to fuse the two complementary information to boost 3d object detection. However, there are still two burning problems for multi-modality 3d object detection. One is the detection problem for the objects with sparse point clouds. The other is the misalignment of different sensors caused by the fixed physical locations. Therefore, this paper argues that explicitly fusing information from the two modalities with the physical misalignment is suboptimal for multi-modality 3d object detection. This paper presents a novel virtual point generation network, VirPNet, to overcome the multi-modality fusion challenges. On one hand, it completes sparse point cloud objects from image source and improves the final detection accuracy. On the other hand, it directly detects 3d targets from raw point clouds to avoid the physical misalignment between LidAR and camera sensors. different from previous point cloud completion methods, VirPNet fully utilizes the geometric information of pixels and point clouds and simplifies 3d point cloud regression into a 2d distance regression problem through a virtual plane. Experimental results on KITTI 3d object detection dataset and nuScenes dataset demonstrate that VirPNet improves the detection accuracy with the help of the generated virtual points.

关键词： 3d object detection autonomous driving geometric information multi-modality fusion virtual point 3d object detection autonomous driving geometric information multi-modality fusion virtual point

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EFMF-pillars: 3d object detection based on enhanced features and multi-scale fusion

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EURASIP JOURNAL ON AdVANCES IN SIGNAL PROCESSING 2024年第1期2024卷 1-18页

作者： Zhang, Wenbiao Chen, Gang Wang, Hongyan Yang, Lina Sun, Tao Zhejiang Sci Tech Univ Coll Informat Sci & Engn Hangzhou 310018 Peoples R China Jiaxing Univ Coll Informat Sci & Engn Jiaxing 314001 Peoples R China Jiaxing Soy Intelligent Co Ltd Jiaxing Peoples R China

As unmanned vehicle technology advances rapidly, obstacle recognition and target detection are crucial links, which directly affect the driving safety and efficiency of unmanned vehicles. In response to the inaccurate localization of small targets such as pedestrians in current object detection tasks and the problem of losing local features in the PointPillars, this paper proposes a three-dimensional object detection method based on improved PointPillars. Firstly, addressing the issue of lost spatial and local information in the PointPillars, the feature encoding part of the PointPillars is improved, and a new pillar feature enhancement extraction module, CSM-Module, is proposed. Channel encoding and spatial encoding are introduced in the new pillar feature enhancement extraction module, fully considering the spatial information and local detailed geometric information of each pillar, thereby enhancing the feature representation capability of each pillar. Secondly, based on the fusion of CSPdarknet and SENet, a new backbone network CSE-Net is designed in this paper, enabling the extraction of rich contextual semantic information and multi-scale global features, thereby enhancing the feature extraction capability. Our method achieves higher detection accuracy when validated on the KITTI dataset. Compared to the original network, the improved algorithm's average detection accuracy is increased by 3.42%, it shows that the method is reasonable and valuable.

关键词： 3d object detection PointPillars CSPdarknet SENet

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Visibility of points: Mining occlusion cues for monocular 3d object detection

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NEUROCOMPUTING 2022年 502卷 48-56页

作者： Chu, Huazhen Mo, Lisha Wang, Rongquan Hu, Tianyu Ma, Huimin Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Chengdu Aircraft Design & Res Inst Chengdu Peoples R China

Monocular 3d object detection aims at achieving prediction from two-dimensional image plane to three-dimensional physical world. It is an inevitable problem that occlusion phenomena limit the performance in practice. To solve the challenging problem that directly represents the spatial information of occlusion relation, we propose the visibility states of points to describe the spatial distance relationships of occlusion pairs and the implied orientation information. The visibility state introduction can better represent the level and direction of occlusion information and enhance the network's understanding of occlusion information. Furthermore, we redesign an end-to-end detector to encode features of visibility states to integrate occlusion ordering cues of the whole image to assist object localization in world space. Experiments on the KITTI3d dataset indicate that our method succeeds in establishing visibility states as occlusion cues and promoting the performance of the original detector. Our method is effective, and the performance is comparable with state-of-the-art approaches, especially outstanding in Moderate and Hard cases. Specifically, our method improves the accuracy of 3d moderate case detection to 42.75% and hard case to 37.03% in the KITTI3d dataset. (C) 2022 Elsevier B.V. All rights reserved.

关键词： 3d object detection Occlusion deep neural network

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Exploring diversity-Based Active Learning for 3d object detection in Autonomous driving

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 15454-15466页

作者： Lin, Jinpeng Liang, Zhihao deng, Shengheng Cai, Lile Jiang, Tao Li, Tianrui Jia, Kui Xu, Xun Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 610032 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China Weride Guangzhou 510700 Huangpu Peoples R China ASTAR Inst Infocomm Res I2R Singapore 138632 Singapore Chengdu Univ Tradit Chinese Med Sch Intelligent Med Chengdu 610075 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen Peoples R China

3d object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3d bounding box annotation. In this work, we investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. Given limited annotation budget, only the most informative frames and objects are automatically selected for human to annotate. Technically, we take the advantage of the multimodal information provided in an AV dataset, and propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We benchmark the proposed method against other AL strategies under realistic annotation cost measurements, where the realistic costs for annotating a frame and a 3d bounding box are both taken into consideration. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly.

关键词： Three-dimensional displays object detection Costs Annotations detectors Uncertainty diversity reception Feature extraction Autonomous vehicles Point cloud compression Active learning 3d object detection autonomous driving

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MFF-Net: Multimodal Feature Fusion Network for 3d object detection

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Computers, Materials & Continua 2023年第6期75卷 5615-5637页

作者： Peicheng Shi Zhiqiang Liu Heng Qi Aixi Yang School of Mechanical Engineering Anhui Polytechnic UniversityWuhu241000Anhui ProvinceChina School of Mechanical Polytechnic Institute of Zhejiang UniversityHangzhou310000Zhejiang ProvinceChina

In complex traffic environment scenarios,it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in *** accuracy of 3d object detection will be affected by problems such as illumination changes,object occlusion,and object detection *** this purpose,we face these challenges by proposing a multimodal feature fusion network for 3d object detection(MFF-Net).In this research,this paper first uses the spatial transformation projection algorithm to map the image features into the feature space,so that the image features are in the same spatial dimension when fused with the point cloud ***,feature channel weighting is performed using an adaptive expression augmentation fusion network to enhance important network features,suppress useless features,and increase the directionality of the network to ***,this paper increases the probability of false detection and missed detection in the non-maximum suppression algo-rithm by increasing the one-dimensional *** far,this paper has constructed a complete 3d target detection network based on multimodal feature *** experimental results show that the proposed achieves an average accuracy of 82.60%on the Karlsruhe Institute of Technology and Toyota Technological Institute(KITTI)dataset,outperforming previous state-of-the-art multimodal fusion *** Easy,Moderate,and hard evaluation indicators,the accuracy rate of this paper reaches 90.96%,81.46%,and 75.39%.This shows that the MFF-Net network has good performance in 3d object detection.

关键词： 3d object detection multimodal fusion neural network autonomous driving attention mechanism

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3d object detection for LidAR Based on Improved PV-RCNN 3

3D Object Detection for LiDAR Based on Improved PV-RCNN

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3rd International Conference on Electronics and Information Technology, EIT 2024

作者： Yang, Feng Xu, Yanli Shanghai Maritime University School of Information Engineering Shanghai China

ISBN: (纸本)9798350369151

Addressing the current issue of poor performance in detecting small targets by lidar-based 3d object detection algorithms that combine point and voxel data, this paper proposes an improved PV-RCNN object detection algorithm named CBAM-RCNN. This algorithm introduces the Convolutional Block Attention Module (CBAM) to refine the Bird's Eye View (BEV) features after voxel compression. It assigns attention weights to the channel and spatial dimensions of the feature maps to enhance the expressive power of the features and subsequently improve the model's detection accuracy for small targets. Experiments on the KITTI dataset demonstrate that CBAM-RCNN achieves a 3.32% and 1.74% increase in mean Average Precision (mAP) for pedestrians and cyclists, respectively, compared to the baseline algorithm, verifying its effectiveness. Compared to current mainstream 3d object detection algorithms, CBAM-RCNN still exhibits better detection performance, further validating its advancement. © 2024 IEEE.

关键词： 3d object detection CBAM LidAR PV-RCNN

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SEGANet: 3d object detection with shape-enhancement and geometry-aware network

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COMPUTERS & ELECTRICAL ENGINEERING 2023年 110卷

作者： Zhou, Jing Hu, Yiyu Lai, Zhongyuan Wang, Tianjiang Jianghan Univ Sch Artificial Intelligence Wuhan 430056 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China

3d object detection approaches from point clouds develop rapidly. However, the distribution of point clouds is unbalanced in the real scene, and thus the distant or occluded objects suffer from too few points to be perceived. This case damages the overall detection accuracy. Hence, we propose a novel two-stage 3d object detection framework, the Shape-Enhancement and Geometry-Aware Network (SEGANet), which aims to mitigate the negative impact of unbalanced point distribution for boosting detection performance. In stage 1, we first capture fine-grained structural knowledge with the assistance of point-wise features from voxels to generate proposals. And in stage 2, we construct a shape enhancement module to reconstruct complete surface points for objects within proposals, then establish an elaborate geometric relevance-aware Transformer module to aggregate high-correlated feature pairs of reconstructed-known parts and decode vital geometric relations of aggregated features. Thus, critical geometric clues are supplied for objects from the data and feature levels, achieving enhanced features for box refinement. Extensive experiments on KITTI and Waymo datasets show that SEGANet achieves low model complexity and excellent detection accuracy, especially leading the baseline method by 2.18% gain in overall detection accuracy and 1.8% gain in average accuracy of weakly sensing objects. This verifies that SEGANet effectively alleviates the impact of point imbalance to significantly boost detection performance.

关键词： 3d object detection Weakly sensing objects Point cloud completion Sparse attention Transformer

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Adaptive and azimuth-aware fusion network of multimodal local features for 3d object detection

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NEUROCOMPUTING 2020年 411卷 32-44页

作者： Tian, Yonglin Wang, Kunfeng Wang, Yuang Tian, Yulin Wang, Zilei Wang, Fei-Yue Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Univ Sci & Technol Beijing Beijing 100083 Peoples R China North China Univ Technol Beijing 100144 Peoples R China

This paper focuses on the construction of strong local features and the effective fusion of image and LidAR data for 3d object detection. We adopt different modalities of LidAR data to generate rich features and present an adaptive and azimuth-aware network to aggregate local features from image, bird's eye view maps and point cloud. Our network mainly consists of three subnetworks: ground plane estimation network, region proposal network and adaptive fusion network. The ground plane estimation network extracts features of point cloud and predicts the parameters of a plane which are used for generating abundant 3d anchors. The region proposal network generates features of image and bird's eye view maps to output region proposals. To integrate heterogeneous image and point cloud features, the adaptive fusion network explicitly adjusts the intensity of multiple local features and achieves the orientation consistency between image and LidAR data by introducing an azimuth-aware fusion module. Experiments are conducted on KITTI dataset and the results validate the advantages of our aggregation of multimodal local features and the adaptive fusion network. (C) 2020 Published by Elsevier B.V.

关键词： 3d object detection Point cloud Multimodal fusion Ground plane fitting

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Pseudo-Mono for Monocular 3d object detection in Autonomous driving

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IEEE TRANSACTIONS ON CIRCUITS ANd SYSTEMS FOR VIdEO TECHNOLOGY 2023年第8期33卷 3962-3975页

作者： Tao, Chongben Cao, Jiecheng Wang, Chen Zhang, Zufeng Gao, Zhen Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Wuhan Elect Informat Inst Wuhan 430019 Hubei Peoples R China McMaster Univ Fac Engn Hamilton ON L8S 0A3 Canada

Current monocular 3d object detection algorithms generally suffer from inaccurate depth estimation, which leads to reduction of detection accuracy. The depth error from image-to-image generation for the stereo view is insignificant compared with the gap in single-image generation. Therefore, a novel pseudo-monocular 3d object detection framework is proposed, which is called Pseudo-Mono. Particularly, stereo images are brought into monocular 3d detection. Firstly, stereo images are taken as input, then a lightweight depth predictor is used to generate the depth map of input images. Secondly, the left input images obtained from stereo camera are used as subjects, which generate enhanced visual feature and multi-scale depth feature by depth indexing and feature matching probabilities, respectively. Finally, sparse anchors set by the foreground probability maps and the multi-scale feature maps are used as reference points to find the suitable initialization approach of object query. The encoded visual feature is adopted to enhance object query for enabling deep interaction between visual feature and depth feature. Compared with popular monocular 3d object detection methods, Pseudo-Mono is able to achieve richer fine-grained information without additional data input. Extensive experimental results on the datasets of KITTI, NuScenes, and MS-COCO demonstrate the generalizability and portability of the proposed method. The effectiveness and efficiency of Pseudo-Mono have been demonstrated by extensive ablation experiments. Experiments on a real vehicle platform have shown that the proposed method maintains high performance in complex real-world environments.

关键词： 3d object detection stereo images transformer sparse anchor point multi-scale feature

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SMIFormer: Learning Spatial Feature Representation for 3d object detection from 4d Imaging Radar via Multi-View Interactive Transformers

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SENSORS 2023年第23期23卷 9429页

作者： Shi, Weigang Zhu, Ziming Zhang, Kezhi Chen, Huanlei Yu, Zhuoping Zhu, Yu Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China Shanghai Motor Vehicle Inspect Certificat & Tech I Shanghai 201805 Peoples R China

4d millimeter wave (mmWave) imaging radar is a new type of vehicle sensor technology that is critical to autonomous driving systems due to its lower cost and robustness in complex weather. However, the sparseness and noise of point clouds are still the main problems restricting the practical application of 4d imaging radar. In this paper, we introduce SMIFormer, a multi-view feature fusion network framework based on 4d radar single-modal input. SMIFormer decouples the 3d point cloud scene into 3 independent but interrelated perspectives, including bird's-eye view (BEV), front view (FV), and side view (SV), thereby better modeling the entire 3d scene and overcoming the shortcomings of insufficient feature representation capabilities under single-view built from extremely sparse point clouds. For multi-view features, we proposed multi-view feature interaction (MVI) to exploit the inner relationship between different views by integrating features from intra-view interaction and cross-view interaction. We evaluated the proposed SMIFormer on the View-of-delft (Vod) dataset. The mAP of our method reached 48.77 and 71.13 in the fully annotated area and the driving corridor area, respectively. This shows that 4d radar has great development potential in the field of 3d object detection.

关键词： 4d imaging radar point cloud 3d object detection voxel feature decoupling multi-view feature interaction deep learning autonomous driving

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