Method

Real-time one stage Monocular 3D Object Detection with 2d-3d consistency [MTMono3d]


Submitted on 8 Jul. 2020 08:58 by
lihong huang (holomatic)

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
This work follows the idea of smoke to directly
predict 3D results with the code reconstructed
completely. We add more data augmentation and
support heavily truncated object whose center is
invisible in camera, in addition, we also predict
the occlusion level which helps 3d depth
perdiction by applying 2d bbox and 3d bbox
consistency.
Parameters:
base learning rate 2.5e-4
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) 90.55 % 83.11 % 75.48 %
Car (Orientation) 90.34 % 82.65 % 74.98 %
Car (3D Detection) 18.54 % 12.44 % 10.09 %
Car (Bird's Eye View) 27.00 % 18.54 % 15.71 %
Pedestrian (Detection) 69.01 % 52.96 % 46.18 %
Pedestrian (Orientation) 54.28 % 41.63 % 36.32 %
Pedestrian (3D Detection) 2.40 % 2.05 % 1.68 %
Pedestrian (Bird's Eye View) 3.11 % 2.38 % 1.89 %
Cyclist (Detection) 67.12 % 47.71 % 38.84 %
Cyclist (Orientation) 55.32 % 39.06 % 31.70 %
Cyclist (3D Detection) 1.59 % 0.90 % 0.96 %
Cyclist (Bird's Eye View) 2.06 % 1.30 % 1.06 %
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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