Method

SparseLiDAR_fusion [SparseLiDAR_fusion]


Submitted on 3 Sep. 2022 03:43 by
Chaofeng Ji (Xi’an Jiaotong University)

Running time:0.08 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We propose a new end-to-end 3D object detection framework by combining a monocular camera with a cheap 4-beam LiDAR
Parameters:
alpha=0.2
Latex Bibtex:

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) 93.77 % 82.91 % 70.83 %
Car (Orientation) 93.09 % 81.36 % 69.19 %
Car (3D Detection) 38.06 % 28.93 % 24.14 %
Car (Bird's Eye View) 54.10 % 41.51 % 34.14 %
Pedestrian (Detection) 50.48 % 35.95 % 31.71 %
Pedestrian (Orientation) 36.95 % 25.80 % 22.67 %
Pedestrian (3D Detection) 11.41 % 7.71 % 6.38 %
Pedestrian (Bird's Eye View) 12.59 % 8.23 % 6.82 %
Cyclist (Detection) 48.74 % 33.00 % 28.68 %
Cyclist (Orientation) 29.88 % 20.27 % 17.93 %
Cyclist (3D Detection) 5.89 % 3.02 % 2.50 %
Cyclist (Bird's Eye View) 7.77 % 4.26 % 3.45 %
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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