High Dimensional Space Model for Dense Monocular Surface Recovery

Vladislav Golyanik, Didier Stricker

In: 3DVision 2017 |. International Conference on 3DVision (3DV-17) 5th October 10-12 Qingdao China Conference Publishing Services (CPS) IEEE Xplore and CSDL 12/2017.


Dense surface reconstruction from monocular image sequences — known as Non-Rigid Structure from Motion (NRSfM) — is a highly ill-posed inverse problem. The objective of NRSfM is to learn 3D shapes from 2D point tracks in an unsupervised manner. While existing methods rely on low-rank models, we propose the concept of High Dimensional Space Model (HDSM). In HDSM, time-varying geometry is encoded by a high-dimensional static structure projected into different metric subspaces. To express nonrigid deformations, instead of directly modelling in the 3D space, we gradually increase space dimensionality as the complexity of the scene increases. HDSM allows for a compact representation with deformation localisation and can be interpreted as a generalisation of the previously proposed models for NRSfM. Relying on HDSM, we develop an algorithm for dense monocular surface recovery. Experiments show that the proposed method achieves high accuracy while allowing for the fine-grained control.


PID5039815.pdf (pdf, 13 MB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz