Method

3D-CVF at SPA [la] [3D-CVF at SPA]
https://github.com/rasd3/3D-CVF

Submitted on 26 Apr. 2020 18:14 by
Yecheol Kim (Hanyang University)

Running time:0.06 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@article{3D-CVF,
title={3D-CVF: Generating Joint Camera and
LiDAR
Features Using Cross-View Spatial Feature
Fusion for
3D Object Detection},
author={J. H. Yoo and Y. Kim and J. S. Kim and
J. W.
Choi
},
journal={ECCV},
year={2020},
}

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) 96.78 % 93.36 % 86.11 %
Car (Orientation) 40.44 % 39.79 % 36.10 %
Car (3D Detection) 89.20 % 80.05 % 73.11 %
Car (Bird's Eye View) 93.52 % 89.56 % 82.45 %
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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