Method

(Monocular) Proposals with Shape Reconstruction [MonoPSR]


Submitted on 17 Nov. 2018 09:30 by
Jason Ku (University of Toronto)

Running time:0.2 s
Environment:GPU @ 3.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{ku2019monopsr,
title = {Monocular 3D Object Detection Leveraging
Accurate Proposals and Shape Reconstruction},
author = {Jason Ku* and Alex D Pon* and Steven L
Waslander},
booktitle = {CVPR},
year = {2019}}

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) 90.18 % 88.84 % 71.44 %
Car (Orientation) 89.88 % 87.83 % 70.48 %
Car (3D Detection) 12.57 % 10.85 % 9.06 %
Car (Bird's Eye View) 20.25 % 17.66 % 15.78 %
Pedestrian (Detection) 85.93 % 68.91 % 60.83 %
Pedestrian (Orientation) 70.56 % 56.30 % 49.84 %
Pedestrian (3D Detection) 12.65 % 10.66 % 10.08 %
Pedestrian (Bird's Eye View) 14.27 % 11.22 % 10.54 %
Cyclist (Detection) 79.80 % 68.99 % 60.19 %
Cyclist (Orientation) 58.93 % 49.30 % 43.45 %
Cyclist (3D Detection) 13.43 % 11.01 % 9.93 %
Cyclist (Bird's Eye View) 14.75 % 12.17 % 11.35 %
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.
This figure as: png eps pdf txt gnuplot




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