TY  - GEN
AV  - public
SN  - 2475-7888
Y1  - 2024/06/12/
TI  - DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction With an Optimizable Feature Grid
CY  - Davos, Switzerland
UR  - http://dx.doi.org/10.1109/3dv62453.2024.00046
ID  - discovery10194974
EP  - 830
N2  - We propose DynamicSurf, a model-free neural implicit surface reconstruction method for high-fidelity 3D modelling of non-rigid surfaces from monocular RGB-D video. To cope with the lack of multi-view cues in monocular sequences of deforming surfaces, one of the most challenging settings for 3D reconstruction, DynamicSurf exploits depth, surface normals, and RGB losses to improve reconstruction fidelity and optimisation time. DynamicSurf learns a neural deformation field that maps a canonical representation of the surface geometry to the current frame. We depart from current neural non-rigid surface reconstruction models by designing the canonical representation as a learned feature grid which leads to faster and more accurate surface reconstruction than competing approaches that use a single MLP. We demonstrate DynamicSurf on public datasets and show that it can optimize sequences of varying frames with 6в speedup over pure MLP-based approaches while achieving comparable results to the state-of-the-art methods.
SP  - 820
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
A1  - Mohamed, M
A1  - Agapito, L
PB  - IEEE
KW  - Geometry
KW  -  Surface reconstruction
KW  -  Solid modeling
KW  - 
Three-dimensional displays
KW  -  Deformation
KW  - Network topology
KW  - 
Dynamics
ER  -