First Steps Toward the Automatic Registration of Deformable Scans (original) (raw)

Non-rigid registration for deformable objects

2014 International Conference on Computer Graphics Theory and Applications (GRAPP), 2014

We present an efficient algorithm for non-rigid registration of two partially overlapping 3D surfaces in which a target surface is a deformed instance of a source surface. The algorithm is implemented in two main phases. In the first phase, the robust algorithm that is used is based on a probability density estimation to find reliable correspondences between the two surfaces. Then, in the second phase, a deformation algorithm is applied for non-rigid registration where the displacement of each point is described by an affine transformation in relation with other points of the same surface and its corresponding point on the other surface. Combined with initial correspondences in the first phase, an effective strategy for optimization of a cost function is carried out to align the two surfaces without using any assumption and user-intervention on the algorithm. We test the robustness of our method by efficiently aligning pairs of surfaces of realistic scan data of human body models.

Registration of deformable objects using a depth camera

2016

This paper describes a method for registration and tracking of deformable objects from points clouds taken from depth cameras. Our method uses a reference model of the object in order to detect rigid and deformed regions in the input cloud. It is based on the fact that deformed objects normally have areas that are not affected by the deformations. These parts are found iteratively allowing to register the object using a chain of rigid transformations. Deformed regions are detected as those that do not satisfy rigidity constrains. Results show that correspondences of points belonging to both rigid and deformed regions can be accurately established with the reference model even in cluttered scenes.

The correlated correspondence algorithm for un-supervised registration of nonrigid surfaces

2005

We present an unsupervised algorithm for registering 3D surface scans of an object undergoing significant deformations. Our algorithm does not use markers, nor does it assume prior knowledge about object shape, the dynamics of its deformation, or scan alignment. The algorithm registers two meshes by optimizing a joint probabilistic model over all point-topoint correspondences between them. This model enforces preservation of local mesh geometry, as well as more global constraints that capture the preservation of geodesic distance between corresponding point pairs. The algorithm applies even when one of the meshes is an incomplete range scan; thus, it can be used to automatically fill in the remaining surfaces for this partial scan, even if those surfaces were previously only seen in a different configuration. We evaluate the algorithm on several real-world datasets, where we demonstrate good results in the presence of significant movement of articulated parts and non-rigid surface deformation. Finally, we show that the output of the algorithm can be used for compelling computer graphics tasks such as interpolation between two scans of a non-rigid object and automatic recovery of articulated object models. * A results video is available at

Geometric Registration Based on Distortion Estimation

2013 IEEE International Conference on Computer Vision, 2013

Surface registration plays a fundamental role in many applications in computer vision, and aims at finding a oneto-one correspondence between surfaces. Conformal mapping based surface registration method maps 3D surfaces onto 2D domains and perform the matching on the 2D plane.

Deformable 3D Shape Registration Based on Local Similarity Transforms

Computer Graphics Forum, 2011

In this paper, a new method for deformable 3D shape registration is proposed. The algorithm computes shape transitions based on local similarity transforms which allows to model not only as-rigid-as-possible deformations but also local and global scale. We formulate an ordinary differential equation (ODE) which describes the transition of a source shape towards a target shape. We assume that both shapes are roughly pre-aligned (e.g., frames of a motion sequence). The ODE consists of two terms. The first one causes the deformation by pulling the source shape points towards corresponding points on the target shape. Initial correspondences are estimated by closestpoint search and then refined by an efficient smoothing scheme. The second term regularizes the deformation by drawing the points towards locally defined rest positions. These are given by the optimal similarity transform which matches the initial (undeformed) neighborhood of a source point to its current (deformed) neighborhood. The proposed ODE allows for a very efficient explicit numerical integration. This avoids the repeated solution of large linear systems usually done when solving the registration problem within general-purpose non-linear optimization frameworks. We experimentally validate the proposed method on a variety of real data and perform a comparison with several state-of-the-art approaches.

A Generic Local Deformation Model for Shape Registration

In this paper, we propose a new surface registration approach using a generic deformation model, which is efficient to compute and flexible to represent arbitrary local shape deformations. From Riemannian geometry, local deformation at each point of a surface can be characterized by the eigenvalues of a special transformation matrix between two canonically parameterized domains. This local transformation specifies all the deformations (i.e., diffeomorphisms) between surfaces while being independent of both intrinsic (parametrization) and extrinsic (embedding) representations. In particular, we show that existing deformation representations (e.g., isometry or conformality) can be viewed as special cases of the proposed local deformation model. Furthermore, a computationally efficient, closed-form solution is derived in the discrete setting via finite element discretization. Based on the proposed deformation model, the shape registration problem is formulated as a high-order Markov Ra...

Isometric registration of ambiguous and partial data

2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009

This paper introduces a new shape matching algorithm for computing correspondences between 3D surfaces that have undergone (approximately) isometric deformations. The new approach makes two main contributions: First, the algorithm is, unlike previous work, robust to "topological noise" such as large holes or "false connections", which is both observed frequently in real-world scanner data. Second, our algorithm samples the space of feasible solutions such that uncertainty in matching can be detected explicitly. We employ a novel randomized feature matching algorithm in order to find robust subsets of geodesics to verify isometric consistency. The paper shows shape matching results for real world and synthetic data sets that could not be handled using previous deformable matching algorithms.

Shape registration in implicit spaces using information theory and free form deformations

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

We present a novel, variational and statistical approach for shape registration. Shapes of interest are implicitly embedded in a higher-dimensional space of distance transforms. In this implicit embedding space, registration is formulated in a hierarchical manner: the Mutual Information criterion supports various transformation models and is optimized to perform global registration; then, a B-spline-based Incremental Free Form Deformations (IFFD) model is used to minimize a Sum-of-Squared-Differences (SSD) measure and further recover a dense local nonrigid registration field. The key advantage of such framework is twofold: 1) it naturally deals with shapes of arbitrary dimension (2D, 3D, or higher) and arbitrary topology (multiple parts, closed/open) and 2) it preserves shape topology during local deformation and produces local registration fields that are smooth, continuous, and establish one-to-one correspondences. Its invariance to initial conditions is evaluated through empirical validation, and various hard 2D/3D geometric shape registration examples are used to show its robustness to noise, severe occlusion, and missing parts. We demonstrate the power of the proposed framework using two applications: one for statistical modeling of anatomical structures, another for 3D face scan registration and expression tracking. We also compare the performance of our algorithm with that of several other well-known shape registration algorithms. Index Terms-Shape registration, mutual information, free form deformations, correspondences, implicit shape representation, distance transforms, partial differential equations. ae 1 INTRODUCTION S HAPE registration is critical to various imaging and vision applications [1]. Global registration, also known as shape alignment, aims to recover a global transformation that brings the pose of a source shape as close as possible to that of a target shape. The alignment has extensive uses in recognition, indexing and retrieval, and tracking. To further account for important local deformations, nonrigid local registration is needed to establish dense correspondences between the basic elements of shapes, such as points, curvature, etc. Medical imaging is a domain that requires local registration such as in building statistical models for internal organs [2], and intrasubject or atlas registration of 2D/3D anatomical structures. There has been a lot of previous research on the shape registration problem [3], [4], [5], as well as on similar problems such as shape matching [6], [2], [7], [8], and point set matching [9]. The algorithms proposed differ in the following three main aspects.