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You searched for subject:(Point Cloud Alignment). Showing records 1 – 3 of 3 total matches.

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Universitat de Girona

1. Roure Garcia, Ferran. Tools for 3D point cloud registration.

Degree: Departament d'Arquitectura i Tecnologia de Computadors, 2017, Universitat de Girona

En aquesta tesi, hem fet una revisió en profunditat de l'estat de l'art del registre 3D, avaluant els mètodes més populars. Donada la falta d'estandardització de la literatura, també hem proposat una nomenclatura i una classificació per tal d'unificar els sistemes d'avaluació i poder comparar els diferents algorismes sota els mateixos criteris. La contribució més gran de la tesi és el Toolbox de Registre, que consisteix en un software i una base de dades de models 3D. El software presentat aquí consisteix en una Pipeline de registre 3D escrit en C++ que permet als investigadors provar diferents mètodes, així com afegir-n'hi de nous i comparar-los. En aquesta Pipeline, no només hem implementat els mètodes més populars de la literatura, sinó que també hem afegit tres mètodes nous que contribueixen a millorar l'estat de l'art de la tecnologia. D'altra banda, la base de dades proporciona una sèrie de models 3D per poder dur a terme les proves necessàries per validar el bon funcionament dels mètodes. Finalment, també hem presentat una nova estructura de dades híbrida especialment enfocada a la cerca de veïns. Hem testejat la nostra proposta conjuntament amb altres estructures de dades i hem obtingut resultats molt satisfactoris, superant en molts casos les millors alternatives actuals. Totes les estructures testejades estan també disponibles al nostre Pipeline. Aquesta Toolbox està pensada per ésser una eina útil per tota la comunitat i està a disposició dels investigadors sota llicència Creative-Commons Advisors/Committee Members: [email protected] (authoremail), false (authoremailshow), Salvi, Joaquim (director), Diez, Yago (director), true (authorsendemail).

Subjects/Keywords: 3D registration; Registre 3D; Registro 3D; Point cloud; Nuvol de punts; Nube de puntos; 3D alignment; Alineament 3D; Alineamiento 3D; 004; 68

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Roure Garcia, F. (2017). Tools for 3D point cloud registration. (Thesis). Universitat de Girona. Retrieved from http://hdl.handle.net/10803/403345

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Roure Garcia, Ferran. “Tools for 3D point cloud registration.” 2017. Thesis, Universitat de Girona. Accessed April 22, 2021. http://hdl.handle.net/10803/403345.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Roure Garcia, Ferran. “Tools for 3D point cloud registration.” 2017. Web. 22 Apr 2021.

Vancouver:

Roure Garcia F. Tools for 3D point cloud registration. [Internet] [Thesis]. Universitat de Girona; 2017. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/10803/403345.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Roure Garcia F. Tools for 3D point cloud registration. [Thesis]. Universitat de Girona; 2017. Available from: http://hdl.handle.net/10803/403345

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Mobahi, Hossein. Optimization by Gaussian smoothing with application to geometric alignment.

Degree: PhD, 0112, 2013, University of Illinois – Urbana-Champaign

It is well-known that global optimization of a nonconvex function, in general, is computationally intractable. Nevertheless, many objective functions that we need to optimize may be nonconvex. In practice, when working with such a nonconvex function, a very natural heuristic is to employ a coarse-to-fine search for the global optimum. A popular deterministic procedure that exemplifies this idea can be summarized briefly as follows. Consider an unconstrained optimization task of minimizing some nonconvex function. One starts from a highly smoothed version of the objective function and hopes that the smoothing eliminates most spurious local minima. More ideally, one hopes that the highly smoothed function would be a convex function, whose global minimum can be found efficiently. Once the minimum of the smoothed function is found, one could gradually reduce the smoothing effect and follow the continuous path of the minimizer, eventually towards a minimum of the objective function. Empirically, people have observed that the minimum found this way has high chance to be the global minimum. Despite its empirical success, there has been little theoretical understanding about the effect of smoothing on optimization. This work rigorously studies some of the fundamental properties of the smoothing technique. In particular, we present a formal definition for the functions that can eventually become convex by smoothing. We present extremely simple sufficient condition for asymptotic convexity as well as a very simple form for an asymptotic minimizer. Our sufficient conditions hold when the objective function satisfies certain decay conditions. Our initial interest for studying this topic arise from its well-known use in geometric image alignment. The alignment problem can be formulated as an optimization task that minimizes the visual difference between the images by searching the space of transformations. Unfortunately, the cost function associated to this problem usually contains many local minima. Thus, unless very good initialization is provided, simple greedy optimization may lead to poor results. To improve the attained solution for the alignment task, we propose smoothing the objective function of the alignment task. In particular, we derive the theoretically correct image blur kernels that arise from (Gaussian) smoothing an alignment objective function. We show that, for smoothing the objective of common motion models, such as affine and homography, there exists a corresponding integral operator on the image space. We refer to the kernels of such integral operators as transformation kernels. Thus, instead of convolving the objective function with a Gaussian kernel in transformation space, we can equivalently compute an integral transform in the image space, which is much cheaper to compute. Advisors/Committee Members: Ma, Yi (advisor), Ma, Yi (Committee Chair), Huang, Thomas S. (committee member), Forsyth, David A. (committee member), Hoiem, Derek W. (committee member), Soatto, Stefano (committee member).

Subjects/Keywords: Nonconvex Optimization; Homotopy Continuation; Image Alignment; Point Cloud Alignment; Coarse-to-fine Optimization

point clouds. The surface of the alignment objective for these problems typically has a lot… …for example affine alignment, using the same form of kernel on 2D images as well as 3D point… …x7B;f : Rn → R}. . . . . 22 Basin of attraction for scale alignment. Egg shape input… …respectively by -1 and 1 intensity values. Obviously, the correct alignment is attained at θ = −1… …the dataset provided in [1]. Bottom: NCC value after alignment. Horizontal axis is… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mobahi, H. (2013). Optimization by Gaussian smoothing with application to geometric alignment. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/42330

Chicago Manual of Style (16th Edition):

Mobahi, Hossein. “Optimization by Gaussian smoothing with application to geometric alignment.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 22, 2021. http://hdl.handle.net/2142/42330.

MLA Handbook (7th Edition):

Mobahi, Hossein. “Optimization by Gaussian smoothing with application to geometric alignment.” 2013. Web. 22 Apr 2021.

Vancouver:

Mobahi H. Optimization by Gaussian smoothing with application to geometric alignment. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/2142/42330.

Council of Science Editors:

Mobahi H. Optimization by Gaussian smoothing with application to geometric alignment. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/42330


Queens University

3. Taati, Babak. Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces .

Degree: Electrical and Computer Engineering, 2009, Queens University

We formulate Local Shape Descriptor selection for model-based object recognition in range data as an optimization problem and offer a platform that facilitates a solution. The goal of object recognition is to identify and localize objects of interest in an image. Recognition is often performed in three phases: point matching, where correspondences are established between points on the 3-D surfaces of the models and the range image; hypothesis generation, where rough alignments are found between the image and the visible models; and pose refinement, where the accuracy of the initial alignments is improved. The overall efficiency and reliability of a recognition system is highly influenced by the effectiveness of the point matching phase. Local Shape Descriptors are used for establishing point correspondences by way of encapsulating local shape, such that similarity between two descriptors indicates geometric similarity between their respective neighbourhoods. We present a generalized platform for constructing local shape descriptors that subsumes a large class of existing methods and allows for tuning descriptors to the geometry of specific models and to sensor characteristics. Our descriptors, termed as Variable-Dimensional Local Shape Descriptors, are constructed as multivariate observations of several local properties and are represented as histograms. The optimal set of properties, which maximizes the performance of a recognition system, depend on the geometry of the objects of interest and the noise characteristics of range image acquisition devices and is selected through pre-processing the models and sample training images. Experimental analysis confirms the superiority of optimized descriptors over generic ones in recognition tasks in LIDAR and dense stereo range images.

Subjects/Keywords: Computer Vision ; Range Data ; Object Recognition ; Tracking ; Local Shape Descriptor ; Point Matching ; Pose Estimation ; Pose Acquisition ; 3-D ; 3D ; Point Cloud ; Satellite Tracking ; Optimization ; Range Image Processing ; Range Image ; RANSAC ; Registration ; Alignment ; Surface ; Computational Geometry ; Detection ; Localization ; Model-Based ; Object Identification ; Point Correspondence ; Feature Selection ; Variable-Dimensional Local Shape Descriptors ; VD-LSD ; LSD ; Genetic Algorithm ; Simulated Annealing ; Forward Feature Selection ; Multivariate Features ; Subset Selection ; Local Properties ; LIDAR ; Dense Stereo ; Stereo ; Precision ; Feature Matching ; Machine Learning ; Training ; Learning Phase ; Preprocessing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Taati, B. (2009). Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces . (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/5107

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Taati, Babak. “Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces .” 2009. Thesis, Queens University. Accessed April 22, 2021. http://hdl.handle.net/1974/5107.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Taati, Babak. “Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces .” 2009. Web. 22 Apr 2021.

Vancouver:

Taati B. Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces . [Internet] [Thesis]. Queens University; 2009. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/1974/5107.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Taati B. Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces . [Thesis]. Queens University; 2009. Available from: http://hdl.handle.net/1974/5107

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.