Method

Semantic Guided Depth Net [SDNet]
https://arxiv.org/abs/1907.10659

Submitted on 10 Apr. 2019 12:09 by
Matthias Ochs (Goethe Universität Frankfurt am Main)

Running time:0.2 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
This network estimates the depth and the
semantic
labels simultaneously. We use the ASPP module
from
DeepLabv3+ to extract dense feature to estimate
these values. The predicted depths are
discretized
to depth class labels.
Parameters:
Base LR: 0.0002
Depth classes: 128
Latex Bibtex:
@InProceedings{OchsKretzMester2019,
author = {Ochs, Matthias and Kretz, Adrian
and Mester, Rudolf},
title = {{SDNet}: Semantic Guided Depth
Estimation Network},
booktitle = {German Conference on Pattern
Recognition (GCPR)},
year = {2019}
}

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
51.14 17.74 79.62 50.45
This table as LaTeX

Test Image 0

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Test Image 1

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Test Image 2

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Test Image 3

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Test Image 4

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Test Image 5

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Test Image 6

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Test Image 7

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Test Image 8

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Test Image 9

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