Method

YOLOMono3D: Real-time Monocular 3D Object Detection [YoloMono3D]
[Anonymous Submission]

Submitted on 30 Jun. 2020 08:17 by
[Anonymous Submission]

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

Method Description:
This work follows the idea of M3D-RPN to directly
predict 3D anchors, and the code was completely
reconstructed to have a different definition of
the anchors. Improve on implementation of hill-
climbing algorithms to make inference much faster
(0.05s/pic for each image, including network
inference, post-optimization, and file IO) while
achieving a similar performance boost. U
Parameters:
To be open source. Mono only.
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) 92.37 % 79.63 % 59.69 %
Car (Orientation) 91.43 % 78.50 % 58.80 %
Car (3D Detection) 18.28 % 12.06 % 8.42 %
Car (Bird's Eye View) 26.79 % 17.15 % 12.56 %
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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