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International Symposium on Visual Computing

ISVC 2019: Advances in Visual Computing pp 141–155Cite as

Adaptive Attention Model for Lidar Instance Segmentation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11844))

Abstract

Detecting and categorizing the instances of objects using Lidar scans are of critical importance for highly autonomous vehicles, which are expected to safely and swiftly maneuver through complex urban streets without the intervention of human drivers. In contrast to recent detection-based approaches [6, 10], we formulate the problem as a point-wise segmentation problem and focus on improving the recognition of small objects, which is very challenging due to the low resolution of commercial Lidar systems. Specifically, we propose a novel end-to-end convolutional neural network (CNN) that encapsulates adaptive attention information, and achieve instance segmentation by fusing multiple auxiliary tasks. We examined our algorithms on the 2D projection data derived from KITTI 3D object detection dataset [8] and achieved at least 14.6% improvement in Intersection over Union (IoU) with faster inference time (25.3 ms per Lidar scan) than the state-of-the-art algorithms.

P. Xiong and X. Hao—Both authors contributed equally to this research.

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Notes

  1. 1.

    Society of Automotive Engineers.

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Author information

Authors and Affiliations

  1. Northwestern University, Evanston, IL, 60208, USA

    Peixi Xiong

  2. University of Southern California, Los Angeles, CA, 90089, USA

    Xuetao Hao

  3. SAIC Innovation Center, San Jose, CA, 95134, USA

    Yunming Shao & Jerry Yu

Authors
  1. Peixi Xiong
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  2. Xuetao Hao
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  3. Yunming Shao
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  4. Jerry Yu
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Corresponding author

Correspondence to Peixi Xiong .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Xiong, P., Hao, X., Shao, Y., Yu, J. (2019). Adaptive Attention Model for Lidar Instance Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-33720-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33719-3

  • Online ISBN: 978-3-030-33720-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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