658 research outputs found
Novel Correspondence-based Approach for Consistent Human Skeleton Extraction
This paper presents a novel base-points-driven shape correspondence (BSC) approach to extract skeletons of articulated objects from 3D mesh shapes. The skeleton extraction based on BSC approach is more accurate than the traditional direct skeleton extraction methods. Since 3D shapes provide more geometric information, BSC offers the consistent information between the source shape and the target shapes. In this paper, we first extract the skeleton from a template shape such as the source shape automatically. Then, the skeletons of the target shapes of different poses are generated based on the correspondence relationship with source shape. The accuracy of the proposed method is demonstrated by presenting a comprehensive performance evaluation on multiple benchmark datasets. The results of the proposed approach can be applied to various applications such as skeleton-driven animation, shape segmentation and human motion analysis
An Efficient Approach to Correspondences between Multiple Non-Rigid Parts
Identifying multiple deformable parts on meshes and establishing dense correspondences between them are tasks
of fundamental importance to computer graphics, with applications to e.g. geometric edit propagation and texture
transfer. Much research has considered establishing correspondences between non-rigid surfaces, but little
work can both identify similar multiple deformable parts and handle partial shape correspondences. This paper
addresses two related problems, treating them as a whole: (i) identifying similar deformable parts on a mesh,
related by a non-rigid transformation to a given query part, and (ii) establishing dense point correspondences
automatically between such parts. We show that simple and efficient techniques can be developed if we make the
assumption that these parts locally undergo isometric deformation. Our insight is that similar deformable parts
are suggested by large clusters of point correspondences that are isometrically consistent. Once such parts are
identified, dense point correspondences can be obtained by an iterative propagation process. Our techniques are
applicable to models with arbitrary topology. Various examples demonstrate the effectiveness of our techniques
Topology-Aware Surface Reconstruction for Point Clouds
We present an approach to inform the reconstruction of a surface from a point
scan through topological priors. The reconstruction is based on basis functions
which are optimized to provide a good fit to the point scan while satisfying
predefined topological constraints. We optimize the parameters of a model to
obtain likelihood function over the reconstruction domain. The topological
constraints are captured by persistence diagrams which are incorporated in the
optimization algorithm promote the correct topology. The result is a novel
topology-aware technique which can: 1.) weed out topological noise from point
scans, and 2.) capture certain nuanced properties of the underlying shape which
could otherwise be lost while performing surface reconstruction. We showcase
results reconstructing shapes with multiple potential topologies, compare to
other classical surface construction techniques, and show the completion of
real scan data
3D Shape Descriptor-Based Facial Landmark Detection: A Machine Learning Approach
Facial landmark detection on 3D human faces has had numerous applications in the literature
such as establishing point-to-point correspondence between 3D face models which is itself a
key step for a wide range of applications like 3D face detection and authentication, matching,
reconstruction, and retrieval, to name a few.
Two groups of approaches, namely knowledge-driven and data-driven approaches, have been
employed for facial landmarking in the literature. Knowledge-driven techniques are the
traditional approaches that have been widely used to locate landmarks on human faces. In
these approaches, a user with sucient knowledge and experience usually denes features to
be extracted as the landmarks. Data-driven techniques, on the other hand, take advantage
of machine learning algorithms to detect prominent features on 3D face models. Besides
the key advantages, each category of these techniques has limitations that prevent it from
generating the most reliable results.
In this work we propose to combine the strengths of the two approaches to detect facial
landmarks in a more ecient and precise way. The suggested approach consists of two phases.
First, some salient features of the faces are extracted using expert systems. Afterwards,
these points are used as the initial control points in the well-known Thin Plate Spline (TPS)
technique to deform the input face towards a reference face model. Second, by exploring and
utilizing multiple machine learning algorithms another group of landmarks are extracted.
The data-driven landmark detection step is performed in a supervised manner providing an
information-rich set of training data in which a set of local descriptors are computed and used
to train the algorithm. We then, use the detected landmarks for establishing point-to-point
correspondence between the 3D human faces mainly using an improved version of Iterative
Closest Point (ICP) algorithms. Furthermore, we propose to use the detected landmarks for
3D face matching applications
- …