Deformable Shape Reconstruction from Monocular Video with Manifold Forests

Tao, Lili; Matuszewski, Bogdan J.

doi:10.1007/978-3-642-40261-6_3

Cited by 1 publication

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“…In that work, the structure of data is estimated using Euclidean distances between pairs of data items, whereas the method proposed in this paper learns the structure from the data, based on random forests techniques. This paper updates and extends the work in [33] with the following four main differences: (a) The method presented in this paper has an additional step in the algorithm, solving the problem when some elements of the measurement matrix are missing; (b) Considering the majority of algorithms are based on minimising squared residual of an error function which makes them sensitive to outliers, another improvement is to reduce the effect of outliers by replacing the L 2 estimator by robust M-estimator [26]; (c) A modification of the method is described when only a relatively small number of training shapes is available. This was firstly introduced by the authors in [32] but without random forests manifold learning technique; (d) More comprehensive set of experiments is presented in the experimental section.…”

Section: Noveltysupporting

confidence: 72%

Robust deformable shape reconstruction from monocular video with manifold forests

Tao

Matuszewski²

2016

Machine Vision and Applications

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Existing approaches to recover structure of 3D deformable objects and camera motion parameters from an uncalibrated images assume the object's shape could be modelled well by a linear subspace. These methods have been proven effective and well suited when the deformations are relatively small, but fail to reconstruct the objects with relatively large deformations. This paper describes a novel approach for 3D non-rigid shape reconstruction, based on manifold decision forest technique. The use of this technique can be justified by noting that a specific type of shape variations might be governed by only a small number of parameters, and therefore can be well represented in a lowdimensional manifold. The key contributions of this work are the use of random decision forests for the shape manifold learning and robust metric for calculation of the re-projection error. The learned manifold defines constraints imposed on the reconstructed shapes. Due to a nonlinear structure of the learned manifold, this approach is more suitable to deal with large and complex object deformations when compared to the linear constraints. The robust metric is applied to reduce the effect of measurement outliers on the quality of the reconstruction. In many practical applications outliers cannot be completely removed and therefore the use of robust techniques is of particular practical interest. The proposed method is validated on 2D points sequences projected from the 3D motion capture data for ground truth comparison and also on real 2D video sequences. Experiments show that the newly proposed method provides better performance compared to previously proposed ones, including the robustness with respect to measurement noise, missing measurements and outliers present in the data.

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Section: Noveltysupporting

confidence: 72%