Method

Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection [MonoGeo]
[Anonymous Submission]

Submitted on 17 Mar. 2021 16:23 by
[Anonymous Submission]

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
In this paper, we propose to learn geometry-guided
depth estimation with projective modeling to
advance monocular 3D object detection.
Specifically, a principled geometry formula with
projective modeling of 2D and 3D deep predictions
in monocular 3D object detection network is
devised.
Parameters:
lr=1.25e-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) 95.11 % 90.14 % 80.19 %
Car (Orientation) 94.67 % 89.44 % 79.27 %
Car (3D Detection) 18.85 % 13.81 % 11.52 %
Car (Bird's Eye View) 25.86 % 18.99 % 16.19 %
Pedestrian (Detection) 58.49 % 44.63 % 40.41 %
Pedestrian (Orientation) 52.87 % 39.76 % 35.83 %
Pedestrian (3D Detection) 8.00 % 5.63 % 4.71 %
Pedestrian (Bird's Eye View) 9.54 % 6.77 % 5.83 %
Cyclist (Detection) 65.42 % 50.48 % 42.48 %
Cyclist (Orientation) 55.58 % 42.05 % 35.48 %
Cyclist (3D Detection) 4.73 % 2.93 % 2.58 %
Cyclist (Bird's Eye View) 5.93 % 3.87 % 3.42 %
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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