Shading-Aware Multi-view Stereo

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9907
• 期：1
• 页码：469-485
• 全文大小：8,517 KB
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• 作者单位：Fabian Langguth (17)
  Kalyan Sunkavalli (18)
  Sunil Hadap (18)
  Michael Goesele (17)
  
  17. TU Darmstadt, Darmstadt, Germany
  18. Adobe Research, San Francisco, USA
  
• 丛书名：Computer Vision ¨C ECCV 2016
• ISBN：978-3-319-46487-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9907

文摘

We present a novel multi-view reconstruction approach that effectively combines stereo and shape-from-shading energies into a single optimization scheme. Our method uses image gradients to transition between stereo-matching (which is more accurate at large gradients) and Lambertian shape-from-shading (which is more robust in flat regions). In addition, we show that our formulation is invariant to spatially varying albedo without explicitly modeling it. We show that the resulting energy function can be optimized efficiently using a smooth surface representation based on bicubic patches, and demonstrate that this algorithm outperforms both previous multi-view stereo algorithms and shading based refinement approaches on a number of datasets.