Accurate Depth and Normal Maps from Occlusion-Aware Focal Stack Symmetry
| Autor: | Anna Alperovich, Bastian Goldluecke, Michael Strecke |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
020206 networking & telecommunications 02 engineering and technology Image plane Stack (abstract data type) Robustness (computer science) Occlusion Normal mapping 0202 electrical engineering electronic engineering information engineering Piecewise Benchmark (computing) 020201 artificial intelligence & image processing Computer vision Algorithm design Artificial intelligence business Mathematics |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2017.271 |
| Popis: | We introduce a novel approach to jointly estimate consistent depth and normal maps from 4D light fields, with two main contributions. First, we build a cost volume from focal stack symmetry. However, in contrast to previous approaches, we introduce partial focal stacks in order to be able to robustly deal with occlusions. This idea already yields significanly better disparity maps. Second, even recent sublabel-accurate methods for multi-label optimization recover only a piecewise flat disparity map from the cost volume, with normals pointing mostly towards the image plane. This renders normal maps recovered from these approaches unsuitable for potential subsequent applications. We therefore propose regularization with a novel prior linking depth to normals, and imposing smoothness of the resulting normal field. We then jointly optimize over depth and normals to achieve estimates for both which surpass previous work in accuracy on a recent benchmark. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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