Method

Fast Point R-CNN V1.1 (LiDAR only) [la] [Fast Point R-CNNv1.1]
[Anonymous Submission]

Submitted on 17 Jan. 2019 13:24 by
[Anonymous Submission]

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={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) 90.59 % 89.71 % 88.13 %
Car (3D Detection) 84.28 % 75.73 % 67.39 %
Car (Bird's Eye View) 88.03 % 86.10 % 78.17 %
This table as LaTeX


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



3D object detection results.
This figure as: png eps pdf txt gnuplot



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




eXTReMe Tracker