Method

PS-SVDM without a pre-trained model [PS-SVDM]


Submitted on 25 Apr. 2024 17:25 by
YuGuang Shi (University of Science and Technology Beijing)

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

Method Description:
1
Parameters:
1
Latex Bibtex:
@article{shi2023svdm,
title={SVDM: Single-View Diffusion Model for
Pseudo-Stereo 3D Object Detection},
author={Shi, Yuguang},
journal={arXiv preprint arXiv:2307.02270},
year={2023}
}

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) 94.45 % 86.15 % 77.86 %
Car (Orientation) 94.16 % 85.50 % 76.93 %
Car (3D Detection) 20.83 % 13.49 % 11.18 %
Car (Bird's Eye View) 28.52 % 19.07 % 16.30 %
Pedestrian (Detection) 44.02 % 32.24 % 29.08 %
Pedestrian (Orientation) 32.63 % 23.28 % 20.76 %
Pedestrian (3D Detection) 11.16 % 6.93 % 5.96 %
Pedestrian (Bird's Eye View) 12.70 % 8.11 % 6.84 %
Cyclist (Detection) 46.35 % 30.16 % 26.13 %
Cyclist (Orientation) 30.41 % 19.46 % 16.67 %
Cyclist (3D Detection) 5.56 % 2.92 % 2.36 %
Cyclist (Bird's Eye View) 6.84 % 3.64 % 3.04 %
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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