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BigSFM: Reconstructing the World from Internet Photos Our group is working on building a 3D model of the world from online photo collections, and our research spans several areas, from image features, to large-scale image matching, to structure-from-motion optimization, to applications such as location recognition. This page summarizes our work, has links to code and datasets we have made available, and has a description of each project. Cornell Participants (and alumni): Noah Snavely (Cornell), David Crandall (Indiana University), Daniel Hauagge (Cornell),Kyle Wilson (Cornell), Song Cao (Cornell), Yin Lou (Cornell), Yunpeng Li (EPFL), Andrew Owens (MIT),Johannes Gehrke (Cornell) Dan Huttenlocher (Cornell) Other Collaborators: Sameer Agarwal (University of Washington and Google), Brian Curless (UW), Yasutaka Furukawa (Google), Steve Seitz (UW), Ian Simon (UW), Rick Szeliski (Microsoft Research) We gratefully acknowledge the support of the following sponsors: Sample Videos Datasets Synopsis: Repeated features are common in urban scenes. Papers:
Bundler - Structure from Motion (SfM) for Unordered Image Collections What is Bundler? Bundler is a structure-from-motion (SfM) system for unordered image collections (for instance, images from the Internet) written in C and C++. An earlier version of this SfM system was used in the Photo Tourism project. For structure-from-motion datasets, please see the BigSFM page. Bundler takes a set of images, image features, and image matches as input, and produces a 3D reconstruction of camera and (sparse) scene geometry as output. The system reconstructs the scene incrementally, a few images at a time, using a modified version of the Sparse Bundle Adjustment package of Lourakis and Argyros as the underlying optimization engine. The Bundler source distribution also contains potentially userful implementations of several computer vision algorithms, including: F-matrix estimation Calibrated 5-point relative pose Triangulation of multiple rays Bundler produces sparse point clouds.
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3D reconstruction Process of capturing the shape and appearance of real objects In computer vision and computer graphics, 3D reconstruction is the process of capturing the shape and appearance of real objects. This process can be accomplished either by active or passive methods.[1] If the model is allowed to change its shape in time, this is referred to as non-rigid or spatio-temporal reconstruction.[2] Motivation and applications[edit] The research of 3D reconstruction has always been a difficult goal. Active methods[edit] Active methods, i.e. range data methods, given the depth map, reconstruct the 3D profile by numerical approximation approach and build the object in scenario based on model. Passive methods[edit] Monocular cues methods[edit] Monocular cues methods refer to using one or more images from one viewpoint (camera) to proceed to 3D construction. Photometric Stereo This approach is more sophisticated than the shape-of-shading method. Stereo vision[edit] Problem statement and basics[edit] and . where
Bundle adjustment A sparse matrix obtained when solving a modestly sized bundle adjustment problem. This is the sparsity pattern of a 992×992 normal-equation (i.e. approximate Hessian) matrix. Black regions correspond to nonzero blocks. Given a set of images depicting a number of 3D points from different viewpoints, bundle adjustment can be defined as the problem of simultaneously refining the 3D coordinates describing the scene geometry, the parameters of the relative motion, and the optical characteristics of the camera(s) employed to acquire the images, according to an optimality criterion involving the corresponding image projections of all points. Uses[edit] General approach[edit] Mathematical definition[edit] Bundle adjustment amounts to jointly refining a set of initial camera and structure parameter estimates for finding the set of parameters that most accurately predict the locations of the observed points in the set of available images. 3D points are seen in views and let be the projection of the . .
CloudCompare Un article de Wikipédia, l'encyclopédie libre. CloudCompare 2.7 CloudCompare est un logiciel d'édition et de traitement de nuages de points 3D (ainsi que de maillages surfaciques triangulaires). Il a été initialement créé pour effectuer des comparaisons entre deux nuages de points 3D denses (tels que ceux obtenus avec des scanners lasers) ou entre un nuage et un maillage. Historique[modifier | modifier le code] Le projet CloudCompare a démarré en 2003, lors de la thèse de Daniel Girardeau-Montaut ayant comme sujet la détection de changement sur des données géométriques tridimensionnelles[1] et dans le cadre d'une collaboration entre Telecom ParisTech et la division R&D d'EDF. Fonctionnalités[modifier | modifier le code] CloudCompare fournit un ensemble d'outils standard pour éditer et visualiser des données 3D (nuages ou maillages principalement). CloudCompare peut gérer un nombre illimité de champs scalaires par nuage de points. Entrées/Sorties[modifier | modifier le code]
Photosynth En pratique[modifier | modifier le code] Accès[modifier | modifier le code] Photosynth est toujours accessible gratuitement en mode consultable (page Web via quelques navigateurs et systèmes supportant le player Silverlight Silverlight dont Internet Explorer mais pas Edge), soit en mode création avec un compte Microsoft et l'installation de sa forme logiciel PC dédiée, soit comme application mobile, alors Photosynth est disponible sur Windows Mobile et iOS, cette application fonctionne sur des smartphones équipés d'au moins 512 Mo de mémoire vive et d'un gyroscope[1]. Ceux-ci se connectant au serveurs Microsoft pour l'upload des photographies (traitement en ligne) relié au site Web Photosynth. Généralité[modifier | modifier le code] Ce logiciel permet la visualisation de lieux de tailles relativement réduite (place, bâtiment, objets). Ces photographies sont censées être positionnées précisément les unes par rapport aux autres. Contraintes[modifier | modifier le code]