3D surface reconstruction from a single uncalibrated 2D image

This paper described a simple computation to reconstruct 3D surface using a single uncalibrated 2D image from a digital camera as an image acquisition device that also focused on fast processing. An object is placed on a table with black background for the digital camera to shoot an image of the object. Image segmentation methods are applied in order to obtain the shape of the object from silhouette. The concept Radon transform is adopted to generate sinograms of the object and it is then inverse Radon transform is used to construct 2D cross-section of the object layer by layer. Canny edge detection helps to get the outline of each cross-section and coordinate points are extracted forming 3D point cloud from the image slices. 3D surface of the object is then reconstructed using Delaunay triangulation to connect each point with another. The results obtained from this project are satisfying regarding the processing time with recognizable shape and also strengthened with considerably low percentage error in the calculation for all six objects used in the experiment

VOLUME DETERMINATION OF LEG ULCER USING REVERSE ENGINEERING METHOD

Reverse Engineering is defined as the process of obtaining a geometric CAD model by digitizing the existing objects. In medical application, it is applied to obtain the CAD model of human skin surface. Chronic leg ulcer refers to the wound which does not heal in the predictable period. Approximately 1% of the world population will develop leg ulcers in their lifespa

Building with Drones: Accurate 3D Facade Reconstruction using MAVs

Automatic reconstruction of 3D models from images using multi-view Structure-from-Motion methods has been one of the most fruitful outcomes of computer vision. These advances combined with the growing popularity of Micro Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools ubiquitous for large number of Architecture, Engineering and Construction applications among audiences, mostly unskilled in computer vision. However, to obtain high-resolution and accurate reconstructions from a large-scale object using SfM, there are many critical constraints on the quality of image data, which often become sources of inaccuracy as the current 3D reconstruction pipelines do not facilitate the users to determine the fidelity of input data during the image acquisition. In this paper, we present and advocate a closed-loop interactive approach that performs incremental reconstruction in real-time and gives users an online feedback about the quality parameters like Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We also propose a novel multi-scale camera network design to prevent scene drift caused by incremental map building, and release the first multi-scale image sequence dataset as a benchmark. Further, we evaluate our system on real outdoor scenes, and show that our interactive pipeline combined with a multi-scale camera network approach provides compelling accuracy in multi-view reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and Automation (ICRA '15), Seattle, WA, US

Scalable and efficient video coding using 3D modeling

In this document we present a 3D model-based video coding scheme for streaming static scene video in a compact way but also enabling time and spatial scalability according to network or terminal capability and providing 3D functionalities. The proposed format is based on encoding the sequence of reconstructed models using second generation wavelets, and efficiently multiplexing the resulting geometric, topological, texture and camera motion binary representations. The wavelets decomposition can be adaptive in order to fit to images and scene contents. To ensure time scalability, this representation is based on a common connectivity for all 3D models, which also allows straightforward morphing between successive models ensuring visual continuity at no additional cost. The method proves to be better than previous methods for video encoding of static scenes, even better than state-of-the-art video coders such as H264 (also known as MPEG AVC). Another application of our approach is the fast transmission and real-time visualization of virtual environments obtained by video capture, for virtual or augmented reality, free walk-through in photo-realistic 3D environments, and numerous other image-base applications. / Nous présentons dans ce document un schéma de codage vidéo basé sur des modèles 3D qui permet de compresser efficacement des vidéos de scènes statiques tout en garantissant une scalabilité temporelle et spatiale afin de s'adapter aux capacités du réseau et des terminaux. Le passage par des modèles 3D permettent d'ajouter des fonctionnalités à la vidéo. Le format proposé se base sur l'encodage d'une séquence de modèles 3D extraits à partir de la vidéo en utilisant des ondelettes de seconde génération, et en multiplexant efficacement les représentations binaires résultaants pour la géométrie, la connectivité, la texture et les positions de caméra. La décomposition par ondelettes peut être aadptative afin de s'adapter au contenu des images et de la scène. Afin d'assurer la scalabilité temporelle, cette représentation et basée sur une connectivité commune pour tous les modèles qui permet de plus uu morphing implicite entre les modèles successifs assurant une continuité visuelle. La méthode a permis d'obtenir de meilleurs résultats pour le codage de vidéos de scènes statiques que le codeur vidéo référence de l'état de l'art H264 (également connu sous le nom de MPEG/AVC). Une autre application de notre approche est la transmission rapide et la visualisation temps réel d'environnements virtuels obtenus partir de vidéos pour les réalités augmentée et virtuelle, la navigation photoréalistique dans des environnements 3D et de nombreuses autres applications basées sur les images

Implicit meshes:unifying implicit and explicit surface representations for 3D reconstruction and tracking

This thesis proposes novel ways both to represent the static surfaces, and to parameterize their deformations. This can be used both by automated algorithms for efficient 3–D shape reconstruction, and by graphics designers for editing and animation. Deformable 3–D models can be represented either as traditional explicit surfaces, such as triangulated meshes, or as implicit surfaces. Explicit surfaces are widely accepted because they are simple to deform and render, however fitting them involves minimizing a non-differentiable distance function. By contrast, implicit surfaces allow fitting by minimizing a differentiable algebraic distance, but they are harder to meaningfully deform and render. Here we propose a method that combines the strength of both representations to avoid their drawbacks, and in this way build robust surface representation, called implicit mesh, suitable for automated shape recovery from video sequences. This surface representation lets us automatically detect and exploit silhouette constraints in uncontrolled environments that may involve occlusions and changing or cluttered backgrounds, which limit the applicability of most silhouette based methods. We advocate the use of Dirichlet Free Form Deformation (DFFD) as generic surface deformation technique that can be used to parameterize objects of arbitrary geometry defined as explicit meshes. It is based on the small set of control points and the generalized interpolant. Control points become model parameters and their change causes model's shape modification. Using such parameterization the problem dimensionality can be dramatically reduced, which is desirable property for most optimization algorithms, thus makes DFFD good tool for automated fitting. Combining DFFD as a generic parameterization method for explicit surfaces and implicit meshes as a generic surface representation we obtained a powerfull tool for automated shape recovery from images. However, we also argue that any other avaliable surface parameterization can be used. We demonstrate the applicability of our technique to 3–D reconstruction of the human upper-body including – face, neck and shoulders, and the human ear, from noisy stereo and silhouette data. We also reconstruct the shape of a high resolution human faces parametrized in terms of a Principal Component Analysis model from interest points and automatically detected silhouettes. Tracking of deformable objects using implicit meshes from silhouettes and interest points in monocular sequences is shown in following two examples: Modeling the deformations of a piece of paper represented by an ordinary triangulated mesh; tracking a person's shoulders whose deformations are expressed in terms of Dirichlet Free Form Deformations

Spatially Coherent RANSAC for Multi-Model Fitting

RANSAC [15, 38, 1] is a reliable method for fitting parametric models to sparse data with many outliers. Originally designed for extracting a single model, RANSAC also has variants for fitting multiple models when supported by data. Our main insight is that, in practice, inliers for each model are often spatially coherent — all previous RANSAC-based methods ignore this. Our new method fits an unspecified number of models to data by combining ideas of random sampling and spatial regularization. As in basic RANSAC, we randomly sample data points to generate a set of proposed models (labels). We formulate model selection and inlier classification as a single problem — labeling of triangulated data points. Geometric fit errors and spatial coherence are combined in one MRF-based energy. In contrast to basic RANSAC, inlier classification does not depend on a fixed threshold. Moreover, our optimization framework allows iterative re-estimation of models/inliers with a clear stopping criteria and convergence guarantees. We show that our new method, SCO- RANSAC, can significantly improve results on synthetic and real data supporting multiple linear, affine, and homographic models