A Dataset of Multi-Illumination Images in the Wild
| Autor: | Michaël Gharbi, Miika Aittala, Lukas Murmann, Frédo Durand |
|---|---|
| Přispěvatelé: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Color balance 020207 software engineering 02 engineering and technology Inverse problem Image (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence Scale (map) business High dynamic range ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | arXiv ICCV |
| Popis: | Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. The data simply does not contain the necessary supervisory signals. Multi-illumination datasets are notoriously hard to capture, so the data is typically collected at small scale, in controlled environments, either using multiple light sources, or robotic gantries. This leads to image collections that are not representative of the variety and complexity of real world scenes. We introduce a new multi-illumination dataset of more than 1000 real scenes, each captured in high dynamic range and high resolution, under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three challenging applications: Single-image illumination estimation, image relighting, and mixed-illuminant white balance. United States. Defense Advanced Research Projects Agency. Revolutionary Enhancement of Visibility by Exploiting Active Light-fields Program (Contract HR0011- 16-C-0030) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|