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Sparse LiDAR and Binocular Stereo Fusion Network for 3D Object Detection

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13536))

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Abstract

3D object detection is an essential task in autonomous driving and virtual reality. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information to have high performance. While much lower-cost stereo cameras have been introduced as a promising alternative, there is still a notable performance gap. In this paper, we explore the idea to leverage sparse LiDAR and stereo images obtained by low-cost sensors for 3D object detection. We propose a novel multi-modal attention fusion end-to-end learning framework for 3D object detection, which effectively integrate the complementarities of sparse LiDAR and stereo images. Instead of directly fusing LiDAR and stereo modalities, we introduce a deep attention feature fusion module, which enables interactions between intermediate layers of LiDAR and stereo image paths by exploring the interdependencies of channel features. These fused features connect higher layer features after upsampling and lower layer features from the stereo image pathway and sparse LiDAR pathway. Hence, the fused features have high-level semantics with higher resolution, which is beneficial for the following object detection network. We provide detailed experiments on KITTI benchmark and achieve state-of-the-art performance compared with the low-cost based methods.

This work was supported by the National Natural Science Foundation of China under Grants 61801414, 62072391, Natural Science Foundation of Shandong Province under Grants ZR2020QF108.

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Author information

Authors and Affiliations

  1. Yantai University, Yantai, 264005, Shandong, China

    Weiqing Yan, Kaiqi Su & Jinlai Ren

  2. Beijing Jiaotong University, Beijing, 100044, China

    Runmin Cong

  3. Shandong University, Jinan, 250100, Shandong, China

    Shuai Li

  4. Yantai IRay Technologies Ltd., Co., Yantai, 264006, China

    Shuigen Wang

Authors
  1. Weiqing Yan
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  3. Jinlai Ren
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  4. Runmin Cong
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  6. Shuigen Wang
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Corresponding author

Correspondence to Jinlai Ren .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Yan, W., Su, K., Ren, J., Cong, R., Li, S., Wang, S. (2022). Sparse LiDAR and Binocular Stereo Fusion Network for 3D Object Detection. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-18913-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18912-8

  • Online ISBN: 978-3-031-18913-5

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