An ICP variant using a point-to-line metric

This paper describes PLICP, an ICP (iterative closest/corresponding point) variant that uses a point-to-line metric, and an exact closed-form for minimizing such metric. The resulting algorithm has some interesting properties: it converges quadratically, and in a finite number of steps. The method is validated against vanilla ICP, IDC (iterative dual correspondences), and MBICP (Metric-Based ICP) by reproducing the experiments performed in Minguez et al. (2006). The experiments suggest that PLICP is more precise, and requires less iterations. However, it is less robust to very large initial displacement errors. The last part of the paper is devoted to purely algorithmic optimization of the correspondence search; this allows for a significant speed-up of the computation. The source code is available for download

Robust Non-Rigid Registration with Reweighted Position and Transformation Sparsity

Non-rigid registration is challenging because it is ill-posed with high degrees of freedom and is thus sensitive to noise and outliers. We propose a robust non-rigid registration method using reweighted sparsities on position and transformation to estimate the deformations between 3-D shapes. We formulate the energy function with position and transformation sparsity on both the data term and the smoothness term, and define the smoothness constraint using local rigidity. The double sparsity based non-rigid registration model is enhanced with a reweighting scheme, and solved by transferring the model into four alternately-optimized subproblems which have exact solutions and guaranteed convergence. Experimental results on both public datasets and real scanned datasets show that our method outperforms the state-of-the-art methods and is more robust to noise and outliers than conventional non-rigid registration methods.Comment: IEEE Transactions on Visualization and Computer Graphic

Evaluation of the Convergence Region of an Automated Registration Method for 3D Laser Scanner Point Clouds

Using three dimensional point clouds from both simulated and real datasets from close and terrestrial laser scanners, the rotational and translational convergence regions of Geometric Primitive Iterative Closest Points (GP-ICP) are empirically evaluated. The results demonstrate the GP-ICP has a larger rotational convergence region than the existing methods, e.g., the Iterative Closest Point (ICP)

Color-aware surface registration

Shape registration is fundamental to 3D object acquisition; it is used to fuse scans from multiple views. Existing algorithms mainly utilize geometric information to determine alignment, but this typically results in noticeable misalignment of textures (i.e. surface colors) when using RGB-depth cameras. We address this problem using a novel approach to color-aware registration, which takes both color and geometry into consideration simultaneously. Color information is exploited throughout the pipeline to provide more effective sampling, correspondence and alignment, in particular for surfaces with detailed textures. Our method can furthermore tackle both rigid and non-rigid registration problems (arising, for example, due to small changes in the object during scanning, or camera distortions). We demonstrate that our approach produces significantly better results than previous methods

YERSEL LAZER TARAYICI NOKTA BULUTLARININ BİRLEŞTİRİLMESİ VE JEODEZİK KOORDİNAT SİSTEMİNE DÖNÜŞTÜRÜLMESİ: LİTERATÜR ARAŞTIRMASI

YERSEL LAZER TARAYICI NOKTA BULUTLARININ BİRLEŞTİRİLMESİ VE JEODEZİK KOORDİNAT SİSTEMİNE DÖNÜŞTÜRÜLMESİ: LİTERATÜR ARAŞTIRMASIÖzetYersel lazer tarayıcılarla üç boyutlu (3B) modelleme çalışmalarında, nokta bulutlarının birleştirilmesi en önemli işlem adımlarından birisidir. Bu amaçla bugüne kadar pek çok yöntem geliştirilmiştir, ancak lazer tarayıcı nokta bulutlarının birleştirilmesi hala önemli bir araştırma konusudur. Diğer yandan nokta bulutlarının otomatik birleştirilmesi de ciddi bir araştırma konusudur ve her türlü veri seti için uygulanabilecek standart bir yöntem bulunmamaktadır. Uygulanan yöntemler; otomasyon, doğruluk, hesaplama süresi, nokta yoğunluğu ve ölçü hatalarına duyarlık bakımından farklılıklar göstermektedir. Ayrıca lazer tarama verilerinin başka konumsal verilerle entegrasyonu için jeodezik koordinat sistemi gibi ortak bir koordinat sistemine dönüştürülmesi gerekir. Ölçme tekniği ve lazer tarayıcı aletinin konfigürasyonuna bağlı olarak jeodezik koordinatlandırma yöntemleri değişiklik göstermektedir. Bu çalışmada lazer tarayıcı nokta bulutlarının birleştirilmesinde kullanılan yöntemler sınıflandırılmış ve belirli özellikleri vurgulanmıştır. Böylece nokta bulutlarının birleştirilmesi ve jeodezik koordinat sistemine dönüştürülmesi konusunda uygulayıcı ve araştırmacılara yol gösterici olunması amaçlanmıştır.Anahtar Kelimeler: Yersel lazer tarama, Nokta bulutu, Birleştirme, Üç boyutlu dönüşüm, LIDAR, Jeodezik koordinatlandırma.REGISTRATION AND GEOREFERENCING METHODS FOR POINT CLOUDS OF TERRESTRIAL LASER SCANNER: A REVIEWAbstractPoint cloud registration is bottle neck on three-dimensional (3B) modelling by using terrestrial laser scanner. Many methods have been developed for the registration of point clouds so far. Neverthless, it is still important research topic. Automatic registration of point clouds is also one of the important research topic on three-dimensional modelling. There are no standart methods for applying all type of data sets. The registration methods have different specicifications in respect to automation, accuracy, computation time, point density and susceptiblity from irregular points. On the other hand, the point clouds have to be registered into extensive coordinate system like this geodetic system for the integration with the other spatial data. Georeferencing methods of point clouds change according to measurement methods and configuration of laser scanner instrument. In this study, point cloud registration methods have been classified and emphasized their main properties. Thus, it had to be given informataion for the applicants and researchers about point cloud registration and georeferencing.Keywords: Terrestrial laser scanning, Point cloud, Alignment, Three-dimensional registration, LIDAR, Georeferencing

Report on shape analysis and matching and on semantic matching

In GRAVITATE, two disparate specialities will come together in one working platform for the archaeologist: the fields of shape analysis, and of metadata search. These fields are relatively disjoint at the moment, and the research and development challenge of GRAVITATE is precisely to merge them for our chosen tasks. As shown in chapter 7 the small amount of literature that already attempts join 3D geometry and semantics is not related to the cultural heritage domain. Therefore, after the project is done, there should be a clear ‘before-GRAVITATE’ and ‘after-GRAVITATE’ split in how these two aspects of a cultural heritage artefact are treated.This state of the art report (SOTA) is ‘before-GRAVITATE’. Shape analysis and metadata description are described separately, as currently in the literature and we end the report with common recommendations in chapter 8 on possible or plausible cross-connections that suggest themselves. These considerations will be refined for the Roadmap for Research deliverable.Within the project, a jargon is developing in which ‘geometry’ stands for the physical properties of an artefact (not only its shape, but also its colour and material) and ‘metadata’ is used as a general shorthand for the semantic description of the provenance, location, ownership, classification, use etc. of the artefact. As we proceed in the project, we will find a need to refine those broad divisions, and find intermediate classes (such as a semantic description of certain colour patterns), but for now the terminology is convenient – not least because it highlights the interesting area where both aspects meet.On the ‘geometry’ side, the GRAVITATE partners are UVA, Technion, CNR/IMATI; on the metadata side, IT Innovation, British Museum and Cyprus Institute; the latter two of course also playing the role of internal users, and representatives of the Cultural Heritage (CH) data and target user’s group. CNR/IMATI’s experience in shape analysis and similarity will be an important bridge between the two worlds for geometry and metadata. The authorship and styles of this SOTA reflect these specialisms: the first part (chapters 3 and 4) purely by the geometry partners (mostly IMATI and UVA), the second part (chapters 5 and 6) by the metadata partners, especially IT Innovation while the joint overview on 3D geometry and semantics is mainly by IT Innovation and IMATI. The common section on Perspectives was written with the contribution of all