Surface feature detection and description with applications to mesh matching

Abstract

In this paper we revisit local feature detectors/descriptors developed for 2D images and extend them to the more general framework of scalar fields defined on 2D manifolds. We provide methods and tools to detect and describe features on surfaces equiped with scalar functions, such as photometric information. This is motivated by the growing need for matching and tracking photometric surfaces over temporal sequences, due to recent advancements in multiple camera 3D reconstruction. We propose a 3D feature detector (MeshDOG) and a 3D feature descriptor (MeshHOG) for uniformly triangulated meshes, invariant to changes in rotation, translation, and scale. The descriptor is able to capture the local geometric and/or photometric properties in a succinct fashion. Moreover, the method is defined generically for any scalar function, e.g., local curvature. Results with matching rigid and non-rigid meshes demonstrate the interest of the proposed framework.

Physical Scale Keypoints: Matching and Registration for Combined Intensity/Range Images

“…This means that depth discontinuities are prevalent throughout the data-at large scales caused by building boundaries, at intermediate scales caused by edges of cars and tree trunks, and at small scales caused by window frames. This situation differs from the assumptions of many other related approaches to three-dimensional matching, in which fewer objects are in the data and/or more complete views are available (Wu et al 2008;Zaharescu et al 2009;Frome et al 2004). Thus, the ability to work with discontinuities plays a central role in the design of our algorithm.…”

“…A 3D coordinate frame can now be computed for each keypoint (Smith et al 2007;Zaharescu et al 2009;Wu et al 2008). One axis is taken to be the normal of the extremal vertex.…”

“…Assuming full, uniformly sampled meshes, Zaharescu et al (2009) extended Wu's viewpoint invariant patches to use full 3D gradients and descriptors. They built a scale space of intensities on a mesh using repeated convolutions of a fixed-width 3D Gaussian.…”

Selectively densified 3D object modeling based on regions of interest detection using neural gas networks

“…The detection of points of interest plays a key role for various applications including: matching of objects and shapes (Lee et al 2005;Gal and Cohen-Or 2006;Zaharescu et al 2009;Zou et al 2008), mesh and shape retrieval (Yang et al 2009;Godil and Wagan 2011), mesh analysis (Zhao et al 2013b), mesh segmentation (Zhao et al 2013b;Song et al 2014), adaptive scanning of 3D objects (Payeur et al 2013), scan integration (Song et al 2014), and guiding mesh simplification (Song et al , 2014Lee et al 2005;Monette-Thériault et al 2014). Finally a general evaluation of various 3D keypoint detectors in terms of repeatability, distinctiveness and computational efficiency for object detection, recognition and registration is presented in Yu et al (2013) and for feature-based matching in Tombari et al (2013).…”

“…The corners detected at several scales are then projected back on the 3D surface. In Zaharescu et al (2009), corners are identified as extrema in across scales DoGs, further processed with a Hessian operator to eliminate non-stable responses. Other solutions capitalize on minimum and maximum values for the detection of points of interest.…”