Method

SSL-RTM3D: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training [SSL-RTM3D]


Submitted on 22 May. 2020 16:12 by
peixuan li (University of Chinese Academy of Sciences)

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
In this work, we propose a monocular 3D detection
approach that can be run in real-time and learn in
semi-supervised mode.
Parameters:
DLA-34
Latex Bibtex:
None

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) 96.44 % 91.07 % 81.19 %
Car (Orientation) 96.34 % 90.70 % 80.72 %
Car (3D Detection) 16.73 % 11.45 % 9.92 %
Car (Bird's Eye View) 23.44 % 16.20 % 14.47 %
This table as LaTeX


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



Orientation estimation 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