Method

Stereo CenterNet(DLA34)[st] [SC(DLA34+DCO)]
[Anonymous Submission]

Submitted on 30 Nov. 2020 17:24 by
[Anonymous Submission]

Running time:0.07 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
In this work, we propose a 3D object detection
method us- ing geometric information in stereo
images, called Stereo CenterNet. Stereo
CenterNet predicts the four semantic key points
of the 3D bounding box of the object in space
and uses 2D left right boxes, 3D dimension,
orientation and key points to restore the
bounding box of the object in the 3D space.
Then, we use an improved photometric alignment
module to further optimize the position of the
3D bound- ing box.
Parameters:
\alpha = 0.6
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) 96.61 % 91.27 % 83.50 %
Car (Orientation) 96.54 % 91.02 % 83.15 %
Car (3D Detection) 49.94 % 31.30 % 25.62 %
Car (Bird's Eye View) 62.97 % 42.12 % 35.37 %
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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