Multi-robot systems (MRS) working in open and dynamic environments are expected to deal with uncertain arrival of new tasks and environment changes, by repeatedly adapting the current task allocation and schedule, in order to maintain its performance (e.g., total utility, balance, etc.). This paper presents an adaptive approach to multi-robot task allocation (MRTA), which combines two adaptive measures corresponding to different levels of a MRS: (1) re-allocating at inter-robot level, for balancing task allocation, and improving total utility of the MRS, and (2) re-scheduling at intra-robot level, for maintaining each robot's utility against the influence of both re-allocating and environment changes. Our approach is expected to have significantly higher adaptation power than both re-allocating only and re-scheduling only cases. An experiment is conducted to evaluate our approach's capability of improving balance and total utility of the MRS, under different environment settings and different combinations of re-allocating and re-scheduling.

We present a method for realtime reconstruction of an animating human body,which produces a sequence of deforming meshes representing a given performance captured by a single commodity depth camera. We achieve realtime single-view mesh completion by enhancing the parameterized SCAPE model.Our method, which we call Realtime SCAPE, performs full-body reconstruction without the use of markers.In Realtime SCAPE, estimations of body shape parameters and pose parameters, needed for reconstruction, are decoupled. Intrinsic body shape is first precomputed for a given subject, by determining shape parameters with the aid of a body shape database. Subsequently, per-frame pose parameter estimation is performed by means of linear blending skinning (LBS); the problem is decomposed into separately finding skinning weights and transformations. The skinning weights are also determined offline from the body shape database,reducing online reconstruction to simply finding the transformations in LBS. Doing so is formulated as a linear variational problem;carefully designed constraints are used to impose temporal coherence and alleviate artifacts. Experiments demonstrate that our method can produce full-body mesh sequences with high fidelity.

In this paper, we propose a data-driven approach for realistic deformation of articulated meshes: the wide availability of pose data makes such an approach feasible. We use multiple reference models, combined with a fast linear blending skinning (LBS) method in local spaces. By exploiting information from the reference models, realistic deformation is achieved, yet with computational efficiency comparable to traditional blending methods-our approach is suitable for real-time applications. We demonstrate the effectiveness of our algorithm using various examples.

Feature matching is a challenging problem at the heart of numerous computer graphics and computer vision applications. We present the SuperMatching algorithm for finding correspondences between two sets of features. It does so by considering triples or higher order tuples of points, going beyond the pointwise and pairwise approaches typically used. SuperMatching is formulated using a supersymmetric tensor representing an affinity metric that takes into account feature similarity and geometric constraints between features: Feature matching is cast as a higher order graph matching problem. SuperMatching takes advantage of supersymmetry to devise an efficient sampling strategy to estimate the affinity tensor, as well as to store the estimated tensor compactly. Matching is performed by an efficient higher order power iteration approach that takes advantage of this compact representation. Experiments on both synthetic and real data show that SuperMatching provides more accurate feature matching than other state-of-the-art approaches for a wide range of 2D and 3D features, with competitive computational cost.

We present an implicit approach for pair-wise non-rigid registration of moving and deforming objects. Shapes of interest are implicitly embedded in the 3D implicit vector space. In this implicit embedding space, registration is performed using a global-to-local framework. Firstly, a non-linear optimization functional defined on the vector distance function is used to find the global alignment between shapes. Secondly, an incremental cubic B-spline free form deformation is used to recover the non-rigid transformation parameters. Local non-rigid registration is posed in terms of minimising an energy functional, for which we give a closed-form linear system and solve it using an improved iterative Gauss-Seidel method. Our approach can consistently produce smooth and continuous registration fields, and correctly establish dense one-to-one correspondences. It can naturally deal with both open partial and closed shapes, and imperfect models with gaps and noise, through its use of the implicit vector representation. Experimental results on several datasets demonstrate the robustness of the proposed method.