Method

Multi-view Labelling Object Detector [la] [MLOD]
https://github.com/JianDeng2018/MLOD

Submitted on 23 Sep. 2019 19:49 by
Jian Deng (University of Waterloo)

Running time:0.12 s
Environment:GPU @ 1.5 Ghz (Python)

Method Description:
In the paper
Parameters:
In the paper
Latex Bibtex:
@article{deng2019mlod,
title={MLOD: A multi-view 3D object detection based on robust feature fusion method},
author={Deng, Jian and Czarnecki, Krzysztof},
journal={arXiv preprint arXiv:1909.04163},
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) 94.88 % 89.97 % 84.98 %
Car (3D Detection) 77.24 % 67.76 % 62.05 %
Car (Bird's Eye View) 90.25 % 82.68 % 77.97 %
Pedestrian (Detection) 68.42 % 55.62 % 51.45 %
Pedestrian (3D Detection) 47.58 % 37.47 % 35.07 %
Pedestrian (Bird's Eye View) 55.09 % 45.40 % 41.42 %
Cyclist (Detection) 75.35 % 56.04 % 49.11 %
Cyclist (3D Detection) 68.81 % 49.43 % 42.84 %
Cyclist (Bird's Eye View) 73.03 % 55.06 % 48.21 %
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



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



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