The KITTI Vision Benchmark Suite

Method

Deep Virtual Stereo Odometry [DVSO]

Submitted on 18 Feb. 2018 12:20 by
Nan Yang (TUM)

Running time:		0.1 s
Environment:		GPU @ 2.5 Ghz (C/C++)

Method Description:

Boosting monocular odometry with semi-supervised
monocular depth estimation.

The stereo baselines are tuned to fix the depth
scale inconsistency between the training set and
testing set for the depth prediction network.

Parameters:

Monocular images;
Visual odometry only;
Resolution 512 x 256.

Latex Bibtex:

@InProceedings{yang2018dvso,
author = "N. Yang and R. Wang and J. Stueckler
and D. Cremers",
title = "Deep Virtual Stereo Odometry: Leveraging
Deep Depth Prediction for Monocular Direct Sparse
Odometry",
booktitle = "European Conference on Computer
Vision (ECCV)",
year = "2018",
month = "September",
}

Detailed Results

From all test sequences (sequences 11-21), our benchmark computes translational and rotational errors for all possible subsequences of length (5,10,50,100,150,...,400) meters. Our evaluation ranks methods according to the average of those values, where errors are measured in percent (for translation) and in degrees per meter (for rotation). Details for different trajectory lengths and driving speeds can be found in the plots underneath. Furthermore, the first 5 test trajectories and error plots are shown below.