Method

DSC3D: Deformable Sampling Constraints in Stereo 3D Object Detection for Autonomous Driving [st] [DSC3D]
[Anonymous Submission]

Submitted on 14 Mar. 2023 06:19 by
[Anonymous Submission]

Running time:0.31 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
We present the possible ambiguity in the feature
sampling of the stereo method pipeline and develop a
novel stereo 3D object detection method, named
DSC3D, which achieves satisfactory performance with
no need to introduce additional supervision.
Parameters:
None
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) 96.56 % 88.74 % 76.41 %
Car (Orientation) 95.58 % 87.10 % 74.74 %
Car (3D Detection) 66.46 % 42.54 % 34.04 %
Car (Bird's Eye View) 74.56 % 51.21 % 42.07 %
Pedestrian (Detection) 58.46 % 43.64 % 39.31 %
Pedestrian (Orientation) 50.03 % 36.46 % 32.63 %
Pedestrian (3D Detection) 29.54 % 20.35 % 18.03 %
Pedestrian (Bird's Eye View) 32.40 % 22.72 % 19.09 %
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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