Browsing by Author "Rusinkiewicz, Szymon"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    ECHO: Extended Convolution Histogram of Orientations for Local Surface Description
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Mitchel, Thomas W.; Rusinkiewicz, Szymon; Chirikjian, Gregory S.; Kazhdan, Michael; Benes, Bedrich and Hauser, Helwig
    This paper presents a novel, highly distinctive and robust local surface feature descriptor. Our descriptor is predicated on a simple observation: instead of describing the points in the vicinity of a feature point relative to a reference frame at the feature point, all points in the region describe the feature point relative to their own frames. Isometry invariance is a byproduct of this construction. Our descriptor is derived relative to the extended convolution – a generalization of the standard convolution that allows the filter to adaptively transform as it passes over the domain. As such, we name our descriptor the Extended Convolution Histogram of Orientations (ECHO). It exhibits superior performance compared to popular surface descriptors in both feature matching and shape correspondence experiments. In particular, the ECHO descriptor is highly stable under near‐isometric deformations and remains distinctive under significant levels of noise, tessellation, complex deformations and the kinds of interference commonly found in real data.
  • Thumbnail Image
    Item
    Poisson Surface Reconstruction with Envelope Constraints
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Kazhdan, Misha; Chuang, Ming; Rusinkiewicz, Szymon; Hoppe, Hugues; Jacobson, Alec and Huang, Qixing
    Reconstructing surfaces from scanned 3D points has been an important research area for several decades. One common approach that has proven efficient and robust to noise is implicit surface reconstruction, i.e. fitting to the points a 3D scalar function (such as an indicator function or signed-distance field) and then extracting an isosurface. Though many techniques fall within this category, existing methods either impose no boundary constraints or impose Dirichlet/Neumann conditions on the surface of a bounding box containing the scanned data. In this work, we demonstrate the benefit of supporting Dirichlet constraints on a general boundary. To this end, we adapt the Screened Poisson Reconstruction algorithm to input a constraint envelope in addition to the oriented point cloud. We impose Dirichlet boundary conditions, forcing the reconstructed implicit function to be zero outside this constraint surface. Using a visual hull and/or depth hull derived from RGB-D scans to define the constraint envelope, we obtain substantially improved surface reconstructions in regions of missing data.
  • Thumbnail Image
    Item
    Region-Aware Simplification and Stylization of 3D Line Drawings
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Nguyen, Vivien; Fisher, Matthew; Hertzmann, Aaron; Rusinkiewicz, Szymon; Bermano, Amit H.; Kalogerakis, Evangelos
    Shape-conveying line drawings generated from 3D models normally create closed regions in image space. These lines and regions can be stylized to mimic various artistic styles, but for complex objects, the extracted topology is unnecessarily dense, leading to unappealing and unnatural results under stylization. Prior works typically simplify line drawings without considering the regions between them, and lines and regions are stylized separately, then composited together, resulting in unintended inconsistencies. We present a method for joint simplification of lines and regions simultaneously that penalizes large changes to region structure, while keeping regions closed. This feature enables region stylization that remains consistent with the outline curves and underlying 3D geometry.
  • Thumbnail Image
    Item
    ToonCap: A Layered Deformable Model for Capturing Poses From Cartoon Characters
    (ACM, 2018) Fan, Xinyi; Bermano, Amit H.; Kim, Vladimir G.; Popović, Jovan; Rusinkiewicz, Szymon; Aydın, Tunç and Sýkora, Daniel
    Characters in traditional artwork such as children's books or cartoon animations are typically drawn once, in fixed poses, with little opportunity to change the characters' appearance or re-use them in a different animation. To enable these applications one can fit a consistent parametric deformable model - a puppet - to different images of a character, thus establishing consistent segmentation, dense semantic correspondence, and deformation parameters across poses. In this work we argue that a layered deformable puppet is a natural representation for hand-drawn characters, providing an effective way to deal with the articulation, expressive deformation, and occlusion that are common to this style of artwork. Our main contribution is an automatic pipeline for fitting these models to unlabeled images depicting the same character in various poses. We demonstrate that the output of our pipeline can be used directly for editing and re-targeting animations.