3D NON-RIGID STRUCTURE FROM MOTION BASED ON SPARSE APPROXIMATION IN TRAJECTORY SPACE

Trajectory representation model has been proposed to describe non-rigid deformation. An optimal trajectory space finding algorithm for 3D non-rigid structure from motion (OTSF-NRSFM) based on this model also has been proposed. However, the influence of the wavelet basis selection on the OTSF-NRSFM algorithm has still not been studied. To help OTSF-NRSFM researchers select wavelet basis properly, we investigated the influences of wavelet basis selection. Two typical wavelet bases, DCT basis and WHT basis, are discussed in this paper. The spectrum properties of wavelet basis and feature point trajectory, trajectory representation results on synthetic shark data, OTSF-NRSFM reconstruction results on synthetic data and real data are analyzed. The results show that the wavelet selection has much influence on OTSF-NRSFM reconstruction results of some non-rigid feature points, which have complicated trajectory. This paper gives researchers some inspiration about wavelet basis selection in OTSF-NRSFM algorithm.

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Sparse Approximation for Nonrigid Structure from Motion

Journal of Robotics ◽

10.1155/2015/435385 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Yaming Wang ◽

Xiaomeng Yan ◽

Junbao Zheng ◽

Mingfeng Jiang

Keyword(s):

Least Squares ◽

Approximation Method ◽

Structure From Motion ◽

Sparse Approximation ◽

The Other ◽

Effective Solution ◽

Optimal Result ◽

New Thought ◽

Trajectory Space ◽

Motion Problem

This paper introduces applying a novel sparse approximation method into solving nonrigid structure from motion problem in trajectory space. Instead of generating a truncated traditional trajectory basis, this method uses an atom dictionary which includes a set of overcomplete bases to estimate the real shape of the deformable object. Yet, it still runs reliably and can get an optimal result. On the other hand, it does not need to consider the size of predefined trajectory bases; that is to say, there is no need to truncate the trajectory basis. The mentioned method is very easy to implement and the only trouble which needs to be solved is anL1-regularized least squares problem. This paper not only presents a new thought, but also gives out a simple but effective solution for the nonrigid structure from motion problem.

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Consolidating Segmentwise Non-Rigid Structure from Motion

2019 16th International Conference on Machine Vision Applications (MVA) ◽

10.23919/mva.2019.8757909 ◽

2019 ◽

Cited By ~ 1

Author(s):

Vladislav Golyanik ◽

Andre Jonas ◽

Didier Stricker

Keyword(s):

Structure From Motion ◽

Rigid Structure

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A Benchmark and Evaluation of Non-Rigid Structure from Motion

International Journal of Computer Vision ◽

10.1007/s11263-020-01406-y ◽

2020 ◽

Author(s):

Sebastian Hoppe Nesgaard Jensen ◽

Mads Emil Brix Doest ◽

Henrik Aanæs ◽

Alessio Del Bue

Keyword(s):

Computer Vision ◽

Structure From Motion ◽

State Of The Art ◽

The State ◽

Quality Data ◽

Data Set ◽

Rigid Structure ◽

Public Data ◽

3D Information ◽

Further Development

AbstractNon-rigid structure from motion (nrsfm), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of nrsfm, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse nrsfm. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.

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