Method

RoarNet[la] [RoarNet]
[Anonymous Submission]

Submitted on 21 Jul. 2018 01:25 by
[Anonymous Submission]

Running time:0.1 s
Environment:GPU @ >3.5 Ghz (Python + C/C++)

Method Description:
Collaborated research by Kiwoo Shin from
[Mechanical Systems Control lab@UC Berkeley &
Berkeley Deep Drive], Youngwook Paul Kwon from
[PhantomAI, UC Berkeley]
Kiwoo Shin: kiwoo.shin@berkeley.edu
Youngwook Paul Kwon: young@berkeley.edu
Parameters:
N/A
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) 90.69 % 88.80 % 79.46 %
Car (3D Detection) 83.71 % 73.04 % 59.16 %
Car (Bird's Eye View) 88.20 % 79.41 % 70.02 %
This table as LaTeX


2D object detection results.
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3D object detection results.
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Bird's eye view results.
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