Type
Conference PaperKAUST Department
Visual Computing Center (VCC)KAUST
Electrical Engineering Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2020-01-10Preprint Posting Date
2019-03-05Online Publication Date
2020-01-10Print Publication Date
2019-06Permanent link to this record
http://hdl.handle.net/10754/660667
Metadata
Show full item recordAbstract
Point clouds are challenging to process due to their sparsity, therefore autonomous vehicles rely more on appearance attributes than pure geometric features. However, 3D LIDAR perception can provide crucial information for urban navigation in challenging light or weather conditions. In this paper, we investigate the versatility of Shape Completion for 3D Object Tracking in LIDAR point clouds. We design a Siamese tracker that encodes model and candidate shapes into a compact latent representation. We regularize the encoding by enforcing the latent representation to decode into an object model shape. We observe that 3D object tracking and 3D shape completion complement each other. Learning a more meaningful latent representation shows better discriminatory capabilities, leading to improved tracking performance. We test our method on the KITTI Tracking set using car 3D bounding boxes. Our model reaches a 76.94% Success rate and 81.38% Precision for 3D Object Tracking, with the shape completion regularization leading to an improvement of 3% in both metrics.Citation
Giancola, S., Zarzar, J., & Ghanem, B. (2019). Leveraging Shape Completion for 3D Siamese Tracking. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2019.00145Sponsors
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. RGC/3/3570-01-01.Publisher
IEEEConference/Event name
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)arXiv
1903.01784Additional Links
https://ieeexplore.ieee.org/document/8954383/https://ieeexplore.ieee.org/document/8954383/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8954383
ae974a485f413a2113503eed53cd6c53
10.1109/CVPR.2019.00145