Abstract:Processing signals on surfaces often involves resampling the signal over the vertices of a dense mesh and applying mesh-based filtering operators. We present a framework to process a signal directly in a texture atlas domain. The benefits are twofold: avoiding resampling degradation and exploiting the regularity of the texture image grid. The main challenges are to preserve continuity across atlas chart boundaries and to adapt differential operators to the non-uniform parameterization. We introduce a novel function space and multigrid solver that jointly enable robust, interactive, and geometry-aware signal processing. We demonstrate our approach using several applications including smoothing and sharpening, multiview stitching, geodesic distance computation, and line integral convolution.

Abstract:We convert a sequence of unstructured textured meshes into a mesh with incrementally changing connectivity and atlas parameterization. Like prior work on surface tracking, we seek temporally coherent mesh connectivity to enable efficient representation of surface geometry and texture. Like recent work on evolving meshes, we pursue local remeshing to permit tracking over long sequences containing significant deformations or topological changes. Our main contribution is to show that both goals are realizable within a common framework that simultaneously evolves both the set of mesh triangles and the parametric map. Sparsifying the remeshing operations allows the formation of large spatiotemporal texture charts. These charts are packed as prisms into a 3D atlas for a texture video. Reducing tracking drift using mesh-based optical flow helps improve compression of the resulting video stream.

Abstract:Scanned performances are commonly represented in virtual environments as sequences of textured triangle meshes. Detailed shapes deforming over time benefit from meshes with dynamically evolving connectivity. We analyze these unstructured mesh sequences to automatically synthesize motion graphs with new smooth transitions between compatible poses and actions. Such motion graphs enable natural periodic motions, stochastic playback, and user-directed animations. The main challenge of unstructured sequences is that the meshes differ not only in connectivity but also in alignment, shape, and texture. We introduce new geometry processing techniques to address these problems and demonstrate visually seamless transitions on high-quality captures.

Abstract:This work revisits the Shock Filters of Osher and Rudin and shows how the proposed filtering process can be interpreted as the advection of image values along flow-lines. Using this interpretation, we obtain an efficient implementation that only requires tracing flow-lines and re-sampling the image. We show that the approach is stable, allowing the use of arbitrarily large time steps without requiring a linear solve. Furthermore, we demonstrate the robustness of the approach by extending it to the processing of signals on meshes in 3D.

Abstract:In this work, we introduce a method to do object extraction in RGBD images. Our method consists in a depth based approach which provides an insight into connectedness, proximity and planarity of the scene. We combine the depth and the color in a GraphCut framework to achieve robustness. Specifically, we propose a depth-based seeding which reduces the uncertainty and limitations of the traditional color based seeding. The results of our depth-based seeding were satisfactory and allowed good segmentation results at indoor environments. An extension of our method to do video segmentation using contour graphs is also discussed

Abstract: The main contribution of this work is a continuous domain formulation of subpixel filtering that overcomes some limitations of discrete approaches. The main gain of the continuous formulation is a flexible and precise representation of the model parameters: subpixel geometry, light spectrum, metric color spaces, and visual blurring. The continuous formulation provides an appropiate scenario to accurately identify the effect of these parameters on the optimal kernel.

Abstract: In this talk we discuss the principles of optical flow and introduce a method to align a pair of signals sampled at the vertices of a triangle mesh. We show applications on texture interpolation and photometric tracking.