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Informatik IX
Chair of Computer Vision & Artificial Intelligence

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85748 Garching info@vision.in.tum.de

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Photometric Depth Super-Resolution Dataset

Photometric Depth Super-Resolution

Bjoern Haefner1 Songyou Peng2 Alok Verma1 Yvain Quéau3 Daniel Cremers1
1Technical University of Munich 2University of Illinois at Urbana-Champaign 3GREYC, UMR CNRS 6072

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Special Issue on RGB-D Vision: Methods and Applications

This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image. A single-shot variational approach is first put forward, which is effective as long as the target's reflectance is piecewise-constant. It is then shown that this dependency upon a specific reflectance model can be relaxed by focusing on a specific class of objects (e.g., faces), and delegate reflectance estimation to a deep neural network. A multi-shot strategy based on randomly varying lighting conditions is eventually discussed. It requires no training or prior on the reflectance, yet this comes at the price of a dedicated acquisition setup. Both quantitative and qualitative evaluations illustrate the effectiveness of the proposed methods on synthetic and real-world scenarios.

Code

The code that generated the data shown here is available on github:
https://github.com/BjoernHaefner/DepthSRfromShading
https://github.com/pengsongyou/SRmeetsPS.

Dataset

The following dataset contains RGB-D sequences and reconstructed 3D models of multiple different scenes. We captured the RGB-D data under different scaling factors using an Asus Xtion Pro and an Intel RealSense D415 RGB-D sensor. Please refer to the respective publication when using this data.

Format

For each scene of the Photometric Depth Super-Resolution dataset, we provide the respective RGB-D sequence as well as the refined 3D models. Each RGB-D sequence contains:

  • Color frames
  • Depth frames
  • Masks
  • Intrinsic parameters (default factory calibration).

Rucksack

Android

Download (mat-files):

Download (obj-files):

Basecap

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Minion

Download (mat-files):

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Blanket

Clothes

Monkey

Wool

Face 1

Download (mat-files):

Download (png-file of deep net albedo estimate):

Download (obj-files):

Face 2

Download (mat-files):

Download (png-file of deep net albedo estimate):

Download (obj-files):

Face 3

Download (mat-files):

Download (png-file of deep net albedo estimate):

Download (obj-files):

Face 4

Download (mat-files):

Download (png-file of deep net albedo estimate):

Download (obj-files):

Face 5

Download (mat-files):

Download (png-file of deep net albedo estimate):

Download (obj-files):

Face 6

Download (mat-files):

Download (png-file of deep net albedo estimate):

Download (obj-files):

Tabletcase

Shirt

Backpack

Download (mat-files):

Download (obj-files):

Ovenmitt

Hat

Download (mat-files):

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Vase

Download (mat-files):

Download (obj-files):

License

Unless stated otherwise, all data in the Photometric Depth Super-Resolution Dataset is licensed under a Creative Commons 4.0 Attribution License (CC BY-NC-SA 4.0).


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Journal Articles
2020
[]Photometric Depth Super-Resolution (B. Haefner, S. Peng, A. Verma, Y. Quéau and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 42, 2020. ([supp] [project page] [ieeexplore]) [bibtex] [arXiv:1809.10097] [pdf]
Conference and Workshop Papers
2018
[]Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading (B. Haefner, Y. Quéau, T. Möllenhoff and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ([supp] [poster] [code] [cvf] [video]) [bibtex] [pdf] [video]Spotlight Presentation
2017
[]Depth Super-Resolution Meets Uncalibrated Photometric Stereo (S. Peng, B. Haefner, Y. Quéau and D. Cremers), In International Conference on Computer Vision Workshops (ICCVW), 2017. ([code] [cvf]) [bibtex] [arXiv:1708.00411] [pdf]Oral Presentation at ICCV Workshop on Color and Photometry in Computer Vision
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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:
CVG Group DVL Group

News

25.01.2021

Richard Szeliski (University of Washington) will give a talk in the TUM AI lecture series on Jan 28th, 5pm! Livestream

10.12.2020

Frank Dellaert (Georgia Tech) will give a talk in the TUM AI lecture series on Dec 17th, 4pm! Livestream

15.10.2020

Jon Barron (Google) will give a talk in the TUM AI lecture series on Oct 22nd, 9pm! Livestream

02.10.2020

We have five papers accepted to 3DV 2020!

30.09.2020

Our effcient deep network architectures form the AI engine of the project Slow Down COVID-19 at Harvard.

More