Deformable Shape Reconstruction from Monocular Video with Manifold Forests

Abstract

A common approach to recover structure of 3D deformable scene and camera motion from uncalibrated 2D video sequences is to assume that shapes can be accurately represented in linear subspaces. These methods are simple and have been proven effective for reconstructions of objects with relatively small deformations, but have considerable limitations when the deformations are large or complex. This paper describes a novel approach to reconstruction of deformable objects utilising a manifold decision forest technique. The key contribution of this work is the use of random decision forests for the shape manifold learning. The learned manifold defines constraints imposed on the reconstructed shapes. Due to 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.