TR2010-044

Specular Surface Reconstruction from Sparse Reflection Correspondences


    •  Sankaranarayanan, A., Veeraraghavan, A.N., Tuzel, C.O., Agrawal, A.K., "Specular Surface Reconstruction from Sparse Reflection Correspondences", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2010.
      BibTeX TR2010-044 PDF
      • @inproceedings{Sankaranarayanan2010jun,
      • author = {Sankaranarayanan, A. and Veeraraghavan, A.N. and Tuzel, C.O. and Agrawal, A.K.},
      • title = {Specular Surface Reconstruction from Sparse Reflection Correspondences},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2010,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2010-044}
      • }
  • Research Area:

    Computer Vision

We present a practical approach for surface reconstruction of smooth mirror-like objects using sparse reflection correspondences (RCs). Assuming finite object motion with a fixed camera and un-calibrated environment, we derive the relationship between RC and the surface shape. We show that by locally modeling the surface as a quadric, the relationship between the RCs and unknown surface parameters becomes linear. We develop a simple surface reconstruction algorithm that amounts to solving either an eigen-value problem or a second order cone program (SOCP). Ours is the first method that allows for reconstruction of mirror surfaces from sparse RCs, obtained from standard algorithms such as SIFT. Our approach overcomes the practical issues in shape from specular flow (SFSF) such as the requirement of dense optical flow and undefined/infinite flow at parabolic points. We also show how to incorporate auxiliary information such as sparse surface normals into our framework. Experiments, both real and synthetic are shown that validate the theory presented.

 

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    •  NEWS   CVPR 2010: 8 publications by C. Oncel Tuzel, Tim K. Marks, Yuichi Taguchi, Srikumar Ramalingam, Michael J. Jones and Amit K. Agrawal
      Date: June 13, 2010
      Where: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
      MERL Contacts: Michael Jones; Tim Marks
      Brief
      • The papers "Optimal Coded Sampling for Temporal Super-Resolution" by Agrawal, A.K., Gupta, M., Veeraraghavan, A.N. and Narasimhan, S.G., "Breaking the Interactive Bottleneck in Multi-class Classification with Active Selection and Binary Feedback" by Joshi, A.J., Porikli, F.M. and Papanikolopoulos, N., "Axial Light Field for Curved Mirrors: Reflect Your Perspective, Widen Your View" by Taguchi, Y., Agrawal, A.K., Ramalingam, S. and Veeraraghavan, A.N., "Morphable Reflectance Fields for Enhancing Face Recognition" by Kumar, R., Jones, M.J. and Marks, T.K., "Increasing Depth Resolution of Electron Microscopy of Neural Circuits using Sparse Tomographic Reconstruction" by Veeraraghavan, A., Genkin, A.V., Vitaladevuni, S., Scheffer, L., Xu, S., Hess, H., Fetter, R., Cantoni, M., Knott, G. and Chklovskii, D., "Specular Surface Reconstruction from Sparse Reflection Correspondences" by Sankaranarayanan, A., Veeraraghavan, A.N., Tuzel, C.O. and Agrawal, A.K., "Fast Directional Chamfer Matching" by Liu, M.-Y., Tuzel, C.O., Veeraraghavan, A.N. and Chellappa, R. and "Robust RVM regression using sparse outlier model" by Mitra, K., Veeraraghavan, A. and Chellappa, R. were presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
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