Dense Semi-Rigid Scene Flow Estimation from RGBD images

International audienceScene flow is defined as the motion field in 3D space, and can be computed from a single view when using an RGBD sensor. We propose a new scene flow approach that exploits the local and piecewise rigidity of real world scenes. By modeling the motion as a field of twists, our method encourages piecewise smooth solutions of rigid body motions. We give a general formulation to solve for local and global rigid motions by jointly using intensity and depth data. In order to deal efficiently with a moving camera, we model the motion as a rigid component plus a non-rigid residual and propose an alternating solver. The evaluation demonstrates that the proposed method achieves the best results in the most commonly used scene flow benchmark. Through additional experiments we indicate the general applicability of our approach in a variety of different scenarios

Relational Contracting, Repeated Negotiations, and Hold-Up

We propose a unified framework to study relational contracting and hold-up problems in infinite horizon stochastic games. We first illustrate that with respect to long run decisions, the common formulation of relational contracts as Pareto-optimal public perfect equilibria is in stark contrast to fundamental assumptions of hold-up models. We develop a model in which relational contracts are repeatedly newly negotiated during relationships. Negotiations take place with positive probability and cause bygones to be bygones. Traditional relational contracting and hold-up formulations are nested as opposite corner cases. Allowing for intermediate cases yields very intuitive results and sheds light on many plausible trade-offs that do not arise in these corner cases. We establish a general existence result and a tractable characterization for stochastic games in which money can be transferred. This paper formulates a theory of relational contracting in dynamic games. A crucial feature is that existing relational contracts can depreciate and ensuing negotiations then treat previous informal agreements as bygones. The model nests the traditional formulation of relational contracts as Pareto-optimal equilibria as a special case. In repeated games both formulations are always mathematically equivalent. We provide ample illustrations that in dynamic games the traditional formulation is restrictive in so far that it rules out by assumption many plausible hold-up problems - even for small discount factors. Our model provides a framework that naturally unifies the analysis of relational contracting and hold-up problems

Convex Generalizations of Total Variation based on the Structure Tensor with Applications to Inverse Problems ⋆

Abstract. We introduce a generic convex energy functional that is suitable for both grayscale and vector-valued images. Our functional is based on the eigenvalues of the structure tensor, therefore it penalizes image variation at every point by taking into account the information from its neighborhood. It generalizes several existing variational penalties, such as the Total Variation and vectorial extensions of it. By introducing the concept of patch-based Jacobian operator, we derive an equivalent formulation of the proposed regularizer that is based on the Schatten norm of this operator. Using this new formulation, we prove convexity and develop a dual definition for the proposed energy, which gives rise to an efficient and parallelizable minimization algorithm. Moreover, we establish a connection between the minimization of the proposed convex regularizer and a generic type of nonlinear anisotropic diffusion that is driven by a spatially regularized and adaptive diffusion tensor. Finally, we perform extensive experiments with image denoising and deblurring for grayscale and color images. The results show the effectiveness of the proposed approach as well as its improved performance compared to Total Variation and existing vectorial extensions of it.

A Taxonomy and Evaluation of Dense Light Field Depth Estimation Algorithms

This paper presents the results of the depth estimation challenge for dense light fields, which took place at the second workshop on Light Fields for Computer Vision (LF4CV) in conjunction with CVPR 2017. The challenge consisted of submission to a recent benchmark [7], which allows a thorough performance analysis. While individual results are readily available on the benchmark web page http://www.lightfield-analysis.net, we take this opportunity to give a detailed overview of the current participants. Based on the algorithms submitted to our challenge, we develop a taxonomy of light field disparity estimation algorithms and give a report on the current state-ofthe- art. In addition, we include more comparative metrics, and discuss the relative strengths and weaknesses of the algorithms. Thus, we obtain a snapshot of where light field algorithm development stands at the moment and identify aspects with potential for further improvement

A Taxonomy and Evaluation of Dense Light Field Depth Estimation Algorithms

This paper presents the results of the depth estimation challenge for dense light fields, which took place at the second workshop on Light Fields for Computer Vision (LF4CV) in conjunction with CVPR 2017. The challenge consisted of submission to a recent benchmark [7], which allows a thorough performance analysis. While individual results are readily available on the benchmark web page http://www.lightfield-analysis.net, we take this opportunity to give a detailed overview of the current participants. Based on the algorithms submitted to our challenge, we develop a taxonomy of light field disparity estimation algorithms and give a report on the current state-ofthe- art. In addition, we include more comparative metrics, and discuss the relative strengths and weaknesses of the algorithms. Thus, we obtain a snapshot of where light field algorithm development stands at the moment and identify aspects with potential for further improvement