The KITTI Vision Benchmark Suite

Method

SS3D: Monocular Object Detection and 3D Box Fitting Trained End-to-End [SS3D]

Submitted on 10 Jan. 2019 12:27 by
Eskil Jörgensen (Chalmers University of Technology)

Running time:		48 ms
Environment:		Tesla V100 (Python)

Method Description:

In the SS3D pipeline, an image is fed to a fully-convolutional neural
network, which performs 2D detection and regresses keypoint
locations, orientation, dimensions and distance for each object.
Using
a learned weighting of these outputs, an optimizer yields accurate 3D
bounding boxes.

Parameters:

Latex Bibtex:

@article{DBLP:journals/corr/abs-1906-08070,
author = {Eskil Jörgensen and
Christopher Zach and
Fredrik Kahl},
title = {Monocular 3D Object Detection and Box Fitting Trained
End-to-End Using
Intersection-over-Union Loss},
journal = {CoRR},
volume = {abs/1906.08070},
year = {2019},
url = {http://arxiv.org/abs/1906.08070},
eprint = {1906.08070}
}

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)	92.72 %	84.92 %	70.35 %
Car (Orientation)	92.57 %	84.38 %	69.82 %
Car (3D Detection)	10.78 %	7.68 %	6.51 %
Car (Bird's Eye View)	16.33 %	11.52 %	9.93 %
Pedestrian (Detection)	61.58 %	45.79 %	41.14 %
Pedestrian (Orientation)	53.72 %	39.60 %	35.40 %
Pedestrian (3D Detection)	2.31 %	1.78 %	1.48 %
Pedestrian (Bird's Eye View)	2.48 %	2.09 %	1.61 %
Cyclist (Detection)	52.97 %	35.48 %	31.07 %
Cyclist (Orientation)	42.95 %	27.79 %	24.26 %
Cyclist (3D Detection)	2.80 %	1.45 %	1.35 %
Cyclist (Bird's Eye View)	3.45 %	1.89 %	1.44 %