3D Reconstruction with Globally-Optimized Point Selection

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概要

• 論文の詳細を見る

• This paper proposes a method for reconstructing accurate 3D surface points. To this end, robust and dense reconstruction with Shape-from-Silhouettes (SfS) and accurate multiview stereo are integrated. Unlike gradual shape shrinking and/or bruteforce large space search by existing space carving approaches, our method obtains 3D points by SfS and stereo independently, and then selects correct ones from them. The point selection is achieved in accordance with spatial consistency and smoothness of 3D point coordinates and normals. The globally optimized points are selected by graph-cuts. Experimental results with several subjects containing complex shapes demonstrate that our method outperforms existing approaches and our previous method.

著者

• Ukita Norimichi

  Graduate School Of Information Science Nara Institute Of Science And Technology

• MATSUDA Kazuki

  Graduate School of Information Science, Nara Institute of Science and Technology

関連論文

• Efficient Topological Calibration and Object Tracking with Distributed Pan-Tilt Cameras
• Direct Shape Carving : Smooth 3D Points and Normals for Surface Reconstruction
• 3D Reconstruction with Globally-Optimized Point Selection

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