Publication
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.
Abstract
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.