An evaluation of canonical forms for non-rigid 3D shape retrieval

Canonical forms attempt to factor out a non-rigid shape’s pose, giving a pose-neutral shape. This opens up the possibility of using methods originally designed for rigid shape retrieval for the task of non-rigid shape retrieval. We extend our recent benchmark for testing canonical form algorithms. Our new benchmark is used to evaluate a greater number of state-of-the-art canonical forms, on five recent non-rigid retrieval datasets, within two different retrieval frameworks. A total of fifteen different canonical form methods are compared. We find that the difference in retrieval accuracy between different canonical form methods is small, but varies significantly across different datasets. We also find that efficiency is the main difference between the methods

3D Correspondence by Breadth First Search Frontiers

This paper presents a novel, robust, and fast 3D shape correspondence algorithm applicable to the two snapshots of the same object in arbitrary deformation. Given two such frames as triangle meshes with fixed connectivity, our algorithm first classifies vertices into Breadth-First Search (BFS) frontiers according to their unweighted shortest path distance from a source vertex. This is followed by the rigid or non-rigid alignment of the corresponding frontiers of two meshes as the second and final step. This algorithm is flexible; high-resolution meshes are welcome. It is robust; results approved by human intuition as well as our own numerical correspondence error metric. It is fast; sequential running time turns out to be quadratic in number of vertices, whereas this upper bound can be pulled down as low as subquadratic O(V 1.5 ) once the second step is naturally and easily parallelized. Due to consistent frontier selection, second step does the optimal work of O(V 3 ) Hungarian assignment in less than a quadratic time

Triangulation free 3D Reconstruction from LiDAR Data

We demonstrate a generic method for visualization of high-resolution unorganized and noisy 3D data points with a surface of significantly lower resolution. To this effect, we feed our algorithm with all the go Light Detection And Ranging (LiDAR) data and approximate it with a triangular mesh that is being deformed iteratively until convergence. Our main contribution is avoiding the direct triangulation of LiDAR points, a cumbersome step common in literature

Şekil Deformasyonu ile Duruş Eniyilemesi

Bilgisayar grafiği alanındaki bu projemizde özgün şekil deformasyonu algoritmaları tasarlayarak mevcut 3B modelleri özel duruşları getireceğiz. Bu özel duruşları detayları-koruyan (detail-preservşng) doğal poz (canonical pose) ve 2B eskiz ile örtüşen 3B poz olarak tanımladık. Bu özel pozları elde ettiğimiz takdirde çok önemli uygulamalar geliştirebileceğiz (örn. veritabanından şekil bulup getirme). Ayrıca zorlu 3B modelleme sürecini basite indirgeyerek tüketici konumdaki birçok ilgili kullanıcıyı potansiyel üretici haline getirebileceğiz, ve modelleme sürecini hızlandıracağız. Özgün konulara değineceğimiz bu projemizin sonunda uluslararası saygın dergilerde yayınlar, ve günümüzün popüler oyun ve film endüstrilerinde kullanılabilecek yazılımlar geliştirmeyi hedefliyoruz

Recent advances in shape correspondence

Important new developments have appeared since the most recent direct survey on shape correspondence published almost a decade ago. Our survey covers the period from 2011, their stopping point, to 2019, inclusive. The goal is to present the recent updates on correspondence computation between surfaces or point clouds embedded in 3D. Two tables summarizing and classifying the prominent, to our knowledge, papers of this period, and a large section devoted to their discussion lay down the foundation of our survey. The discussion is carried out in chronological order to reveal the distribution of various types of correspondence methods per year. We also explain our classification criteria along with the most basic solution examples. We finish with conclusions and future research directions

A Genetic Isometric Shape Correspondence Algorithm with Adaptive Sampling

We exploit the permutation creation ability of genetic optimization to find the permutation of one point set that puts it into correspondence with another one. To this end, we provide a genetic algorithm for the 3D shape correspondence problem, which is the main contribution of this article. As another significant contribution, we present an adaptive sampling approach that relocates the matched points based on the currently available correspondence via an alternating optimization. The point sets to be matched are sampled from two isometric (or nearly isometric) shapes. The sparse one-to-one correspondence, i.e., bijection, that we produce is validated both in terms of running time and accuracy in a comprehensive test suite that includes four standard shape benchmarks and state-of-the-art techniques

A marching algorithm for isosurface extraction from face-centered cubic lattices

This work provides a novel method that extracts isosurfaces from face-centered cubic (FCC) lattices. It has been theoretically shown that sampling volumetric data on an FCC lattice tiled with rhombic dodecahedra is more efficient than sampling them on a Cartesian lattice tiled with cubes, in that the FCC lattice can represent the same data set as a Cartesian lattice with the same accuracy, yet with approximately 23% fewer samples. This fact, coupled with the good properties of rhombic dodecahedra, encouraged us to develop this related isosurface extraction technique. Thanks to the sparser sampling required by the FCC lattices, the de facto standard isosurface extraction algorithm, namely marching cubes, is accelerated significantly, as demonstrated. This reduced sampling rate also leads to a decrement in the number of triangles of the extracted models when compared to the marching cubes result. Finally, the topological consistency problem of the original marching cubes algorithm is also resolved. We show the potential of our algorithm with an indirect volume-rendering application

A shape deformation algorithm for constrained multidimensional scaling

We present a new Euclidean embedding technique based on volumetric shape registration. Extrinsic representation of the intrinsic geometry of a shape is preferable in various computer graphics applications as it poses only a small degrees of freedom to deal with during processing. A popular Euclidean embedding approach to achieve such a representation is multidimensional scaling (MDS), which, however, distorts the original geometric details drastically. Our method introduces a constraint on the original MDS formulation in order, to preserve the initial geometric details while the input shape is pulled towards its MDS pose using the perfectly accurate bijection in between. The regularizer of this registration framework is chosen in such a way that the system supports large deformations yet remains fast. Consequently, we produce a detail-preserving MDS pose in 90 s for a 53 K-vertex high-resolution mesh on a modest computer. We can also add pairwise point constraints on the deforming shape without any additional cost. Detail-preserving MDS is superior for non-rigid shape retrieval and useful for shape segmentation, as demonstrated

Skuller: A volumetric shape registration algorithm for modeling skull deformities

We present an algorithm for volumetric registration of 3D solid shapes. In comparison to previous work on image based registration, our technique achieves higher efficiency by leveraging a template tetrahedral mesh. In contrast to point- and surface-based registration techniques, our method better captures volumetric nature of the data, such as bone thickness. We apply our algorithm to study pathological skull deformities caused by a particular condition, i.e., craniosynostosis. The input to our system is a pair of volumetric 3D shapes: a tetrahedral mesh and a voxelized object represented by a set of voxel cells segmented from computed tomography (CT) scans. Our general framework first performs a global registration and then launches a novel elastic registration process that uses as much volumetric information as possible while deforming the generic template tetrahedral mesh of a healthy human skull towards the underlying geometry of the voxel cells. Both data are high-resolution and differ by large non-rigid deformations. Our fully-automatic solution is fast and accurate, as compared with the state of the arts from the reconstruction and medical image registration fields. We use the resulting registration to match the ground-truth surfaces extracted from the medical data as well as to quantify the severity of the anatomical deformity

Shape Interpolation via Multiple Curves

We present a method that interpolates new shapes between a given pair of source and target shapes. To this end, we utilize a database of related shapes that is used to replace the direct transition from the source to the target by a composition of small transitions. This so-called data-driven interpolation scheme proved useful as long as the database is sufficiently large. We advance this idea one step further by processing the database shapes part by part, which in turn enables realistic interpolations with relatively small databases. We obtain promising preliminary results and point out potential improvements that we intend to address in our future work