Method

TANet: Robust 3D Object Detection from Point Clouds with Triple Attention [TANet]
https://github.com/happinesslz/TANet.git

Submitted on 2 Sep. 2019 18:16 by
Zhe Liu (Huazhong University of Science and Technology)

Running time:0.035s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
In this paper, we focus on exploring the robustness of the3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects,e.g., Pedestrians, is unsatisfactory, 2) when adding additional noise points, the performance of existing approaches de-creases rapidly. To alleviate these problems, a novel TANet is introduced in this paper, which mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression (CFR)module. By considering the channel-wise, point-wise and voxel-wise attention jointly, the TA module enhances the crucial information of the target while suppresses the unstable cloud points. Besides, the novel stacked TA further exploits the multi-level feature attention. In addition, the CFR module boosts the accuracy of localization without excessive computation cost. Experimental results on the validation set of KITTI dataset demonstrate that, in the challenging noisy cases, i.e., adding additional random noisy points around each object, the presented approach goes far beyond state-of-the-art approaches, especially for the Pedestrian class.The running speed is around 29 frames per second.
Parameters:
See the paper
Latex Bibtex:
@article{liu2019tanet,
title={TANet: Robust 3D Object Detection from Point Clouds with Triple Attention},
author={Zhe Liu and Xin Zhao and Tengteng Huang and Ruolan Hu and Yu Zhou and Xiang Bai},
year={2020},
journal={AAAI},
url={https://arxiv.org/pdf/1912.05163.pdf},
eprint={1912.05163},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 93.67 % 90.67 % 85.31 %
Car (Orientation) 93.52 % 90.11 % 84.61 %
Car (3D Detection) 84.39 % 75.94 % 68.82 %
Car (Bird's Eye View) 91.58 % 86.54 % 81.19 %
Pedestrian (Detection) 69.90 % 59.07 % 56.44 %
Pedestrian (Orientation) 42.54 % 36.21 % 34.39 %
Pedestrian (3D Detection) 53.72 % 44.34 % 40.49 %
Pedestrian (Bird's Eye View) 60.85 % 51.38 % 47.54 %
Cyclist (Detection) 82.24 % 68.20 % 62.13 %
Cyclist (Orientation) 81.15 % 66.37 % 60.10 %
Cyclist (3D Detection) 75.70 % 59.44 % 52.53 %
Cyclist (Bird's Eye View) 79.16 % 63.77 % 56.21 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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