SHREC 2011: robust feature detection and description benchmark

Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 robust feature detection and description benchmark simulates the feature detection and description stages of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and strength of the transformations that can be dealt with. The present paper is a report of the SHREC'11 robust feature detection and description benchmark results.Comment: This is a full version of the SHREC'11 report published in 3DO

Compression of Textured Surfaces Represented as Surfel Sets

A method for lossy compression of genus-0 surfaces is presented. Geometry, texture and other surface attributes are incorporated in a unified manner. The input surfaces are represented by surfels (surface elements), i.e., by a set of disks with attributes. Each surfel, with its attribute vector, is optimally mapped onto a sphere in the sense of geodesic distance preservation. The resulting spherical vector-valued function is resampled. Its components are decorrelated by the Karhunen-Loève transform, represented by spherical wavelets and encoded using the zerotree algorithm. Methods for geodesic distance computation on surfel-based surfaces are considered. A novel efficient approach to dense surface flattening/mapping, using rectangular distance matrices, is employed. The distance between each surfel and a set of key-surfels is optimally preserved, leading to greatly improved resolution and eliminating the need for interpolation, that complicates and slows down existing surface unfolding methods. Experimental surfel-based surface compression results demonstrate successful compression at very low bit rates

Approximating Geodesics on Point Set Surfaces

We present a technique for computing piecewise linear approximations of geodesics on point set surfaces by minimizing an energy function defined for piecewise linear path. The function considers path length, closeness to the surface for the nodes of the piecewise linear path and for the intermediate line segments. Our method is robust with respect to noise and outliers. In order to avoid local minima, a good initial piecewise linear approximation of a geodesic is provided by Dijkstra’s algorithm that is applied to a proximity graph constructed over the point set. As the proximity graph we use a sphere-of-influence weighted graph extended for surfel sets. The convergence of our method has been studied and compared to results of other methods by running experiments on surfaces whose geodesics can be computed analytically. Our method is presented and optimized for surfel-based representations but it has been implemented also for MLS surfaces. Moreover, it can also be applied to other surface representations, e.g., triangle meshes, radial-basis functions, etc

Multilevel active registration for kinect human body scans: from low quality to high quality

Registration of 3D human body has been a challenging research topic for over decades. Most of the traditional human body registration methods require manual assistance, or other auxiliary information such as texture and markers. The majority of these methods are tailored for high-quality scans from expensive scanners. Following the introduction of the low-quality scans from cost-effective devices such as Kinect, the 3D data capturing of human body becomes more convenient and easier. However, due to the inevitable holes, noises and outliers in the low-quality scan, the registration of human body becomes even more challenging. To address this problem, we propose a fully automatic active registration method which deforms a high-resolution template mesh to match the low-quality human body scans. Our registration method operates on two levels of statistical shape models: (1) the first level is a holistic body shape model that defines the basic figure of human; (2) the second level includes a set of shape models for every body part, aiming at capturing more body details. Our fitting procedure follows a coarse-to-fine approach that is robust and efficient. Experiments show that our method is comparable with the state-of-the-art methods.Comment: 14 pages, the Journal of Multimedia System

SHREC 2011: robust feature detection and description benchmark

© The Eurographics Association 2011. Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 robust feature detection and description benchmark simulates the feature detection and description stages of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and strength of the transformations that can be dealt with. The present paper is a report of the SHREC'11 robust feature detection and description benchmark results.Boyer E., Bronstein A.M., Bronstein M.M., Bustos B., Darom T., Horaud R., Hotz I., Keller Y., Keustermans J., Kovnatsky A., Litman R., Reininghaus J., Sipiran I., Smeets D., Suetens P., Vandermeulen D., Zaharescu A., Zobel V., ''SHREC 2011: robust feature detection and description benchmark'', 4th Eurographics workshop on 3D object retrieval - 3DOR 2011, pp. 71-78, April 10, 2011, Llandudno, UK.status: publishe