Scalable Dense Monocular Surface Reconstruction

Mohammad Dawud Ansari, Vladislav Golyanik, Didier Stricker

In: 3DVision 2017. International Conference on 3DVision (3DV-17) October 10-12 Qingdao China Conference Publishing Services (CPS) IEEE Xplore and CSDL 12/2017.


This paper reports on a novel template-free monocular non-rigid surface reconstruction approach. Existing techniques using motion and deformation cues rely on multiple prior assumptions, are often computationally expensive and do not perform equally well across the variety of data sets. In contrast, the proposed Scalable Monocular Surface Reconstruction (SMSR) combines strengths of several algorithms, i.e., it is scalable with the number of points, can handle sparse and dense settings as well as different types of motions and deformations. We estimate camera pose by singular value thresholding and proximal gradient. Our formulation adopts alternating direction method of multipliers which converges in linear time for large point track matrices. We integrate trajectory space constraints by the measurement matrix smoothing and not for the whole optimisation. In the extensive experiments, SMSR is demonstrated to consistently achieve state-of-the-art accuracy on a wide variety of data sets.

Weitere Links


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence