Method

FailNet: Monocular Camera [FailNet-Mono]
[Anonymous Submission]

Submitted on 29 Jul. 2019 17:00 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Monocular camera 3D object detection model using
point cloud estimation from depth map.
Parameters:
-
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) 57.86 % 48.91 % 42.95 %
Car (Orientation) 24.41 % 20.02 % 17.85 %
Car (3D Detection) 8.64 % 11.58 % 10.14 %
Car (Bird's Eye View) 16.62 % 14.89 % 13.03 %
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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