Method

Self-Supervised Semantically-Guided Depth Estimation [SGDepth]
https://github.com/ifnspaml/SGDepth

Submitted on 11 Nov. 2019 12:32 by
Marvin Klingner (Technische Universität Braunschweig)

Running time:0.1 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
This method combines the training of self-
supervised depth estimation on the KITTI dataset
together with semantic segmentation on the
Cityscapes dataset and has therefore not seen any
KITTI label. Additionally, the method utilizes
several cross-task guidance components.
Parameters:
Latex Bibtex:
@InProceedings{klingner2020selfsupervised,
author = {Klingner, Marvin and Termöhlen,
Jan-Aike and Mikolajczyk, Jonas and Fingscheidt,
Tim},
booktitle = {ECCV},
title = {{Self-Supervised Monocular Depth
Estimation: Solving the Dynamic Object Problem by
Semantic Guidance}},
year = {2020}
}

Detailed Results

This page provides detailed results for the method(s) selected. For the first 20 test images, we display the original image, the color-coded result and an error image. The error image contains 4 colors:
red: the pixel has the wrong label and the wrong category
yellow: the pixel has the wrong label but the correct category
green: the pixel has the correct label
black: the groundtruth label is not used for evaluation

Test Set Average

IoU class iIoU class IoU category iIoU category
53.04 24.36 78.65 55.95
This table as LaTeX

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Test Image 9

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