Abstract

This paper shows how current sparse approaches to Non-Rigid Structure From Motion (NRSfM) can be extended to template-free dense reconstruction. Our contribution can be broken down into two components: (i) We provide asymptotic improvements to current optimisation approaches, allowing them to scale to the dense case. (ii) We remove the need to estimate an initial rest shape or a known 3d template. This both increases the robustness of the method, allowing it to be applied to arbitrary sequences, and further decreases the run-time of our approach. Extending the existing techniques of NRSfM to fullyautomatic and dense template-free reconstruction substantially increases the difficulty of the optimisation problems faced, and we provide novel approaches for avoiding local minima in both discrete and continuous sub-problems.

Keywords:
Maxima and minima Robustness (evolution) Computer science Structure from motion Algorithm Artificial intelligence Motion (physics) Computer vision Mathematics

Metrics

33
Cited By
4.01
FWCI (Field Weighted Citation Impact)
35
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Computational Geometry and Mesh Generation
Physical Sciences →  Computer Science →  Computer Graphics and Computer-Aided Design
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