Method

Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel [SparVox3D]


Submitted on 5 Dec. 2020 14:49 by
Eren Balatkan (Özyeğin University)

Running time:0.05 s
Environment:GPU @ 2.0 Ghz (Python)

Method Description:
We explore Sparse Voxel Representation for
Monocular 3D Object Detection
Parameters:
|alpha=0.2
Latex Bibtex:
@INPROCEEDINGS{9558880, author={Balatkan, Eren and
Kıraç, Furkan}, booktitle={2021 6th International
Conference on Computer Science and Engineering
(UBMK)}, title={Improving Regression Performance
on Monocular 3D Object Detection Using Bin-Mixing
and Sparse Voxel Data}, year={2021}, volume={},
number={}, pages={419-424}, doi=
{10.1109/UBMK52708.2021.9558880}}

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) 83.76 % 67.88 % 52.56 %
Car (3D Detection) 5.27 % 3.20 % 2.56 %
Car (Bird's Eye View) 10.20 % 6.39 % 5.06 %
Pedestrian (Detection) 69.33 % 52.84 % 48.49 %
Pedestrian (3D Detection) 1.93 % 1.35 % 1.04 %
Pedestrian (Bird's Eye View) 2.90 % 2.05 % 1.69 %
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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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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