Alignment Of 3D Models Research Articles

In silico Analysis of PRODH Mutations and their biological significance in disease etiology

In the present study, we performed in silico analysis on all reported mutations of PRODH in order to investigate their biological significance. 3D models of wildtype and mutant PRODH were predicted using I-TASSER. Protein-protein docking was done with Cluspro, while protein-substrate docking was done with Auto Dock tools. Alignment of 3D models (various mutant with wildtype) revealed that Arg185Gln (73.83%) and Gln19Term (6.25%) had the highest and lowest similarity indices, respectively. Enzyme pocket prediction identified the second largest active site pocket containing substrate proline binding residues Leu527, Tyr548, and Arg563. Moreover, docking of mutant and wildtype PRODH with its close interactor ALDH4A1 showed differences with respect to position and nature of interacting amino acids residues. We observed that the nature of amino acid substitution and the number of bonds affect the binding of proline molecule with enzyme, and therefore, affect its biological activity. Keywords: PRODH, proline dehydrogenase enzyme, in silico analysis, modeling and docking, biological significance

Automated Registration of SfM‐MVS Multitemporal Datasets Using Terrestrial and Oblique Aerial Images

AbstractAccurate alignment of 3D models is critical for valid change‐detection analysis from multitemporal photogrammetric datasets. This paper assesses an automated registration strategy which uses the scale‐invariant feature transform (SIFT) algorithm implemented in modern photogrammetric software. This registration solution, also known as “Time‐SIFT”, was tested at two study sites featuring vertical surfaces, including a sea cliff (~500 m2) and a quarry face (~50 000 m2). Tests demonstrated that the investigated registration strategy can achieve accurate alignments between multitemporal point clouds even when using multisource and multi‐perspective data, captured across widely varying spatial and temporal scales and under a range of weather and illumination conditions. The combination of the Time‐SIFT approach with an ICP algorithm produced moderate improvements in the alignment. Furthermore, the use of an innovative direct georeferencing technique, which used the tracking feature of a robotic total station, allowed for accurate georectification of 3D models.

A symmetric objective function for ICP

The Iterative Closest Point (ICP) algorithm, commonly used for alignment of 3D models, has previously been defined using either a point-to-point or point-to-plane objective. Alternatively, researchers have proposed computationally-expensive methods that directly minimize the distance function between surfaces. We introduce a new symmetrized objective function that achieves the simplicity and computational efficiency of point-to-plane optimization, while yielding improved convergence speed and a wider convergence basin. In addition, we present a linearization of the objective that is exact in the case of exact correspondences. We experimentally demonstrate the improved speed and convergence basin of the symmetric objective, on both smooth models and challenging cases involving noise and partial overlap.

Variance and Symmetrical-based Approach for Optimal Alignment of 3D Model

Variance and Symmetrical-based Approach for Optimal Alignment of 3D Model

Variance and Symmetrical-based Approach for Optimal Alignment of 3D Model

Variance and Symmetrical-based Approach for Optimal Alignment of 3D Model

Traffic surveillance camera calibration by 3D model bounding box alignment for accurate vehicle speed measurement

Improved traffic camera calibration method based on vanishing points detection.Automatic scene scale inference by alignment of 3D model bounding box.Extensive experiments on a recent speed measurem...

PTH-103 Evaluation of a novel system for image guided laparoscopic liver surgery in an animal model and first clinical experience

PTH-103 Evaluation of a novel system for image guided laparoscopic liver surgery in an animal model and first clinical experience

A new approach towards image based virtual 3D city modeling by using close range photogrammetry

Abstract. 3D city model is a digital representation of the Earth's surface and it’s related objects such as building, tree, vegetation, and some manmade feature belonging to urban area. The demand of 3D city modeling is increasing day to day for various engineering and non-engineering applications. Generally three main image based approaches are using for virtual 3D city models generation. In first approach, researchers used Sketch based modeling, second method is Procedural grammar based modeling and third approach is Close range photogrammetry based modeling. Literature study shows that till date, there is no complete solution available to create complete 3D city model by using images. These image based methods also have limitations This paper gives a new approach towards image based virtual 3D city modeling by using close range photogrammetry. This approach is divided into three sections. First, data acquisition process, second is 3D data processing, and third is data combination process. In data acquisition process, a multi-camera setup developed and used for video recording of an area. Image frames created from video data. Minimum required and suitable video image frame selected for 3D processing. In second section, based on close range photogrammetric principles and computer vision techniques, 3D model of area created. In third section, this 3D model exported to adding and merging of other pieces of large area. Scaling and alignment of 3D model was done. After applying the texturing and rendering on this model, a final photo-realistic textured 3D model created. This 3D model transferred into walk-through model or in movie form. Most of the processing steps are automatic. So this method is cost effective and less laborious. Accuracy of this model is good. For this research work, study area is the campus of department of civil engineering, Indian Institute of Technology, Roorkee. This campus acts as a prototype for city. Aerial photography is restricted in many country and high resolution satellite images are costly. In this study, proposed method is based on only simple video recording of area. Thus this proposed method is suitable for 3D city modeling. Photo-realistic, scalable, geo-referenced virtual 3D city model is useful for various kinds of applications such as for planning in navigation, tourism, disasters management, transportations, municipality, urban and environmental managements, real-estate industry. Thus this study will provide a good roadmap for geomatics community to create photo-realistic virtual 3D city model by using close range photogrammetry.

Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error

Since its introduction in the early 1990s, the Iterative Closest Point (ICP) algorithm has become one of the most well-known methods for geometric alignment of 3D models. Given two roughly aligned shapes represented by two point sets, the algorithm iteratively establishes point correspondences given the current alignment of the data and computes a rigid transformation accordingly. From a statistical point of view, however, it implicitly assumes that the points are observed with isotropic Gaussian noise. In this paper, we show that this assumption may lead to errors and generalize the ICP such that it can account for anisotropic and inhomogenous localization errors. We 1) provide a formal description of the algorithm, 2) extend it to registration of partially overlapping surfaces, 3) prove its convergence, 4) derive the required covariance matrices for a set of selected applications, and 5) present means for optimizing the runtime. An evaluation on publicly available surface meshes as well as on a set of meshes extracted from medical imaging data shows a dramatic increase in accuracy compared to the original ICP, especially in the case of partial surface registration. As point-based surface registration is a central component in various applications, the potential impact of the proposed method is high.

Alignment of 3D models

In this paper we present a new method for alignment of 3D models. This approach is based on two types of symmetries of the models: the reflective symmetry and the local translational symmetry along a direction. Inspired by the work on the principal component analysis (PCA), we select the best optimal alignment axes within the PCA-axes, the plane reflection symmetry being used as a selection criterion. This pre-processing transforms the alignment problem into an indexing scheme based on the number of the retained PCA-axes. In order to capture the local translational symmetry of a shape along a direction, we introduce a new measure we call the local translational invariance cost ( LTIC). The mirror planes of a model are also used to reduce the number of candidate coordinate frames when looking for the one which corresponds to the user’s perception. Experimental results show that the proposed method finds the rotation that best aligns a 3D mesh.

An Approximate and Efficient Method for Optimal Rotation Alignment of 3D Models

In many shape analysis applications, the ability to find the best rotation that aligns two models is an essential first step in the analysis process. In the past, methods for model alignment have either used normalization techniques, such as PCA alignment, or have performed an exhaustive search over the space of rotation to find the best optimal alignment. While normalization techniques have the advantage of efficiency, providing a quick method for registering two shapes, they are often imprecise and can give rise to poor alignments. Conversely, exhaustive search is guaranteed to provide the correct answer, but, even using efficient signal processing techniques, this type of approach can be prohibitively slow. In this paper, we present a new method for aligning two 3D shapes. We show that the method is markedly faster than existing approaches based on efficient signal processing and we provide registration results demonstrating that the alignments obtained using our method have a high degree of precision and are markedly better than those obtained using normalization.

Development and validation of a generic 3D model of the distal femur

The development and validation of a virtual generic 3D model of the distal femur using computer graphical methods is presented. The synthesis of the generic model requires the following steps: acquisition of bony 3D morphology using standard computed tomography (CT) imaging; alignment of 3D models reconstructed from CT images with a common coordinate system; computer graphical sectioning of the models; extraction of bone contours from the image sections; combining and averaging of extracted contours; and 3D reconstruction of the averaged contours. The generic models reconstructed from the averaged contours of six cadaver femora were validated by comparing their surface geometry on a point to point basis with that of the CT reconstructed reference models. The mean errors ranged from 0.99 to 2.5 mm and were in agreement with the qualitative assessment of the models.