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Robust Surface Reconstruction via Triple Sparsity
Abstract : Reconstructing a surface/image from corrupted gradient fields is a crucial step in many imaging applications where a gradient field is subject to both noise and unlocalized outliers, resulting typically in a non-integrable field. We present in this paper a new optimization method for robust surface reconstruction. The proposed formulation is based on a triple sparsity prior : a sparse prior on the residual gradient field and a double sparse prior on the surface it- self. We develop an efficient alternate minimization strategy to solve the proposed optimization problem. The method is able to recover a good quality surface from severely cor- rupted gradients thanks to its ability to handle both noise and outliers. We demonstrate the performance of the pro- posed method on synthetic and real data. Experiments show that the proposed solution outperforms some existing meth- ods in the three possible cases : noise only, outliers only and mixed noise/outliers.
Cited literature [17 references]
https://hal.inria.fr/hal-00951627
Contributor : H. Yahia <>
Submitted on : Friday, April 4, 2014 - 2:29:13 PM
Last modification on : Wednesday, November 14, 2018 - 1:54:07 PM
Long-term archiving on: : Friday, July 4, 2014 - 12:56:37 PM
Contributor : H. Yahia <>
Submitted on : Friday, April 4, 2014 - 2:29:13 PM
Last modification on : Wednesday, November 14, 2018 - 1:54:07 PM
Long-term archiving on: : Friday, July 4, 2014 - 12:56:37 PM
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cvpr2014_final_hal.pdf
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