Method

Fast Point R-CNN (LiDAR only) [la] [Fast Point R-CNN]


Submitted on 17 Jan. 2019 13:24 by
Yilun Chen (CUHK)

Running time:0.06 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
Fixed some bug and used full dataset for training.
Parameters:
See the paper for details.
Latex Bibtex:
@inproceedings{Chen2019fastpointrcnn,
title={Fast Point R-CNN},
author={Yilun Chen and
Shu Liu and
Xiaoyong Shen and
Jiaya Jia},
booktitle={Proceedings of the IEEE international
conference on computer vision (ICCV)},
year={2019}
}

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) 96.13 % 93.18 % 87.68 %
Car (3D Detection) 85.29 % 77.40 % 70.24 %
Car (Bird's Eye View) 90.87 % 87.84 % 80.52 %
This table as LaTeX

2D object detection results.
This figure as: png eps pdf txt gnuplot



3D object detection results.
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Bird's eye view results.
This figure as: png eps pdf txt gnuplot




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