Method

Monocular 3D Object Detection by Self-Supervised Reconstruction and Uncertainty Propagation [MonoRUn]
[Anonymous Submission]

Submitted on 15 Nov. 2020 15:42 by
[Anonymous Submission]

Running time:0.07 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
We propose a novel monocular 3D detection framework based on dense 2D-3D correspondences,which learns the object 3D geometry in a self-supervised manner and locates the object using PnP algorithm. The framework is fully uncertainty-aware, i.e., it can estimate the object pose covariance from the geometry uncertainty.
Parameters:
TBD
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) 95.48 % 87.91 % 78.10 %
Car (Orientation) 95.44 % 87.64 % 77.75 %
Car (3D Detection) 19.65 % 12.30 % 10.58 %
Car (Bird's Eye View) 27.94 % 17.34 % 15.24 %
Pedestrian (Detection) 73.05 % 56.40 % 51.40 %
Pedestrian (Orientation) 63.28 % 47.82 % 43.23 %
Pedestrian (3D Detection) 10.88 % 6.78 % 5.83 %
Pedestrian (Bird's Eye View) 11.70 % 7.59 % 6.34 %
Cyclist (Detection) 67.47 % 49.13 % 43.41 %
Cyclist (Orientation) 49.04 % 34.36 % 30.22 %
Cyclist (3D Detection) 1.01 % 0.61 % 0.48 %
Cyclist (Bird's Eye View) 1.14 % 0.73 % 0.66 %
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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