Method

C-GCN [C-GCN]


Submitted on 6 Feb. 2020 11:55 by
Silvio Giancola (KAUST)

Running time:0.147 s
Environment:GPU @ V100 (Python)

Method Description:
C-GCN only
Parameters:
3-layer C-GCN
Latex Bibtex:
@article{Zarzar2019PointRGCNGC,
title={PointRGCN: Graph Convolution Networks for 3D
Vehicles Detection Refinement},
author={Jesus Zarzar and Silvio Giancola and Bernard
Ghanem},
journal={ArXiv},
year={2019},
volume={abs/1911.12236}
}

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) 95.64 % 91.73 % 86.37 %
Car (Orientation) 95.63 % 91.57 % 86.13 %
Car (3D Detection) 83.49 % 73.62 % 67.01 %
Car (Bird's Eye View) 91.11 % 86.78 % 80.09 %
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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