Photometric Depth Super-Resolution
| Autor: | Songyou Peng, Daniel Cremers, Alok Verma, Bjoern Haefner, Yvain Quéau |
|---|---|
| Přispěvatelé: | Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU) |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) variational methods Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Iterative reconstruction shape-from-shading Upsampling Photometry (optics) Artificial Intelligence Depth map 0202 electrical engineering electronic engineering information engineering Computer vision Image resolution ComputingMethodologies_COMPUTERGRAPHICS depth super-resolution business.industry RGB-D cameras Applied Mathematics deep learning photometric stereo [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Reflectivity Superresolution Photometric stereo Computational Theory and Mathematics RGB color model 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2020, 42 (10), pp.2453--2464. ⟨10.1109/TPAMI.2019.2923621⟩ |
| ISSN: | 1939-3539 0162-8828 |
| Popis: | 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. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2019. First three authors contribute equally |
| Databáze: | OpenAIRE |
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