Our paper on non-rigid surface recovery is accepted to Pattern Recognition Letters

[2018.03.19]

The following paper is accepted to Pattern Recognition Letters:

  • Non-Rigid Surface Recovery with a Robust Local-Rigidity Prior by Geonho Cha, Minsik Lee, Jungchan Cho, and Songhwai Oh
    • Abstract: It is generally much harder to reconstruct the 3D structures of a non-rigid object than that of a rigid object, because of the increased degrees of freedom. To overcome the ill-posed nature of this problem, there have been many researches that incorporate prior assumptions on the object. An attractive approach is to assume the local parts to be rigid, because there might be a possibility to reduce some degrees of freedom that have to be estimated. Unfortunately, this assumption is not strictly true for general objects, and rigid and non-rigid parts are usually mixed in an object. This issue may be circumvented if we can utilize a robust local-rigidity prior to enforce rigidity on local parts selectively. In this paper, we provide a simple cost function that imposes a rigidity prior on each local triangular patch, and the proposed algorithm minimizes this cost to reconstruct a non-rigid surface. Non-rigid local parts are handled as outliers in the proposed algorithm, which makes the algorithm capable of reconstructing a near-locally-rigid surface that might contain a few non-negligible deformations. Experimental results show that the proposed method provides the state-of-the-art performance for well–known dense surface data sets.