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Using UAV for automatic lithological classification of open pit mining front References 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:
Geological models from photogrammetry | GEOREKA Software With the incredible explosion of drones on the market, more and more uses are starting to be developed. One of the more straight forward applications is creating a 3D capture of outcrops. Using photogrammetry techniques to reconstruct 3D geological models of the drone footage we can use accurate surface information. Drones are already heavily used in the industrial minerals industry where they are used to produce highly detailed DTM’s (digital terrain models) for measuring stockpiles and up-to-date mine surveys for planning. Photogrammetry without drones If drones are not available or not practical (you need a lot of batteries and chargers) in remote areas, why not use your normal camera instead? From outcrop to geological models The big advantage with photogrammetry it that the result will be a 3D model of the surface rather than a stack of photos. Just a 3D outcrop without texture shows much less information After import the outcrop can be used to digitize features like bedding.
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.
Mapping with drones • Exploring the Earth Intro: An exciting part of being a PhD student is being surrounded by new ideas and technology that pushes forward your field of study. On an undergraduate geology field trip to Freycinet National Park, Tasmania, I was part of a team in conjunction with our new TMVC hub (i.e. ARC Industrial Transformation Research Hub, Transforming the Mining Value Chain) at the University of Tasmania, to teach the undergraduate students geological mapping. An aerial done (UAV) with a student mapping in the background – Freycinet Peninsula, Tasmania, Australia Science Spiel: Mapping and drones First published geological map, by William Smith in 1815 Geology is very much an observational-based science. Undergraduate students observing the UAV (i.e. drone) in action. Dr. On a recent field trip for the 2nd year geology undergraduate students, we went to Freycinet Peninsula, on the east coast of Tasmania, to teach them about the rocks that make up the peninsula, and how to do geological mapping. -Stephanie
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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]