Towards high-resolution large-scale multi-view stereo

International audienceBoosted by the Middlebury challenge, the precision of dense multi-view stereovision methods has increased drastically in the past few years. Yet, most methods, although they perform well on this benchmark, are still inapplicable to large-scale data sets taken under uncontrolled conditions. In this paper, we propose a multi-view stereo pipeline able to deal at the same time with very large scenes while still producing highly detailed reconstructions within very reasonable time. The keys to these benefits are twofold: (i) a minimum s-t cut based global optimization that transforms a dense point cloud into a visibility consistent mesh, followed by (ii) a mesh-based variational refinement that captures small details, smartly handling photo-consistency, regularization and adaptive resolution. Our method has been tested on numerous large-scale outdoor scenes. The accuracy of our reconstructions is also measured on the recent dense multi-view benchmark proposed by Strecha et al., showing our results to compare more than favorably with the current state-of-the-art

Use of new technique of image based aimed to perspective return

The diffusion of Image-based 3D modeling techniques, through image-based free, low cost and open source software, have increased drastically in the past few years, especially in Cultural Heritage domain (Architecture, Archeology, Urban planning) [2, 3]. Computer vision techniques use photographs from dataset collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS), the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. Computer vision techniques - Structure from Motion (SfM)- allow to fulfill detailed 3D models from photos dataset collections. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. This research investigates the limits and potentialities of 3D models obtained by using image based techniques in Architectural Heritage field, in order to verify the applicability of the method for the perspective return of the solid perspective of the artistic repertoire in my territory. My approach to this challenging problem is to verify the reliability of the 3Dmodels by Autodesk 123D Catch (web-based package). This paper aimed to demonstrate the efficiency of 123D Catch to obtain an accurate 3D model to operate the perspective return of artistic models

Une modélisation probabiliste de la reconstruction 3D

On s'intéresse dans cet article au problème de la reconstruction 3D d'une scène à partir de 2 photographies de la scène et de la connaissance des relations géométriques entre les points (physique ou virtuelle) de l'espace 3D et les positions dans les 2 images (les calibrations des 2 photographies) d'un point de vue théorique. On introduit une modélisation probabiliste du problème de la reconstruction 3D qui assimile reconstruction 3D et inférence de paramètres inconnus d'un tirage aléatoire. L'expressivité de notre modélisation est discutée pour montrer que la plupart des méthodes classiques de la littérature peuvent se modéliser comme des cas particulier de notre formulation

Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction

Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view images is a fundamental yet active research area in computer vision. Despite the steady progress in multi-view stereo reconstruction, most existing methods are still limited in recovering fine-scale details and sharp features while suppressing noises, and may fail in reconstructing regions with few textures. To address these limitations, this paper presents a Detail-preserving and Content-aware Variational (DCV) multi-view stereo method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising. In reprojection error minimization, we propose a novel inter-image similarity measure, which is effective to preserve fine-scale details of the reconstructed surface and builds a connection between guided image filtering and image registration. In mesh denoising, we propose a content-aware $\ell_{p}$-minimization algorithm by adaptively estimating the $p$ value and regularization parameters based on the current input. It is much more promising in suppressing noise while preserving sharp features than conventional isotropic mesh smoothing. Experimental results on benchmark datasets demonstrate that our DCV method is capable of recovering more surface details, and obtains cleaner and more accurate reconstructions than state-of-the-art methods. In particular, our method achieves the best results among all published methods on the Middlebury dino ring and dino sparse ring datasets in terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image processin

Non-rigid Shape Matching Using Geometry and Photometry

International audienceIn this paper, we tackle the problem of finding correspondences between three-dimensional reconstructions of a deformable surface at different time steps. We suppose that (i) the mechanical underlying model imposes time-constant geodesic distances between points on the surface; and that (ii) images of the real surface are available. This is for instance the case in spatio-temporal shape from videos (e.g. multi-view stereo, visual hulls, etc.) when the surface is supposed approximatively unstretchable. These assumptions allow to exploit both geometry and photometry. In particular we propose an energy based formulation of the problem, extending the work of Bronstein et of. [1]. On the one hand, we show that photometry (i) improves accuracy in case of locally elastic deformations or noisy surfaces and (ii) allows to still find the right solution when [1] fails because of ambiguities (e.g. symmetries). On the other hand, using geometry makes it possible to match shapes that have undergone large motion, which is not possible with usual photometric methods. Numerical experiments prove the efficiency of our method on synthetic and real data

Reconstructing urban scene 3D using VisualSfM

New computer vision techniques use photo dataset to rapidly build detailed 3D models. Computer- vision researchers have explored many approaches to city-scale 3D reconstruc on. Among these systems stands out VisualSfM developed by the University of Washington & Google Inc. It is a open source GUI applica on of a Structure from Mo on (SfM) so! ware that uses a feature extractor called Si! GPU and the Mul core Bundle Adjustment. In addi on it embeds the CMVS/ PMVS2 able to reconstruct dense 3D point cloud. Our goal is to demonstrate the metric accuracy of VisualSfM+CMVS/PMVS2 and that to get run it, you can use an unstructured photo dataset but the result improves if you use a structured photo dataset. The approach has been tested on several large datasets with structured images

IMAGE-BASED MODELING TECHNIQUES FOR ARCHITECTURAL HERITAGE 3D DIGITALIZATION: LIMITS AND POTENTIALITIES

3D reconstruction from images has undergone a revolution in the last few years. Computer vision techniques use photographs from data set collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS), the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. Among all the possibilities we can mainly distinguish desktop and web-based packages. Those last ones offer the opportunity to exploit the power of cloud computing in order to carry out a semi-automatic data processing, thus allowing the user to fulfill other tasks on its computer; whereas desktop systems employ too much processing time and hard heavy approaches. Computer vision researchers have explored many applications to verify the visual accuracy of 3D model but the approaches to verify metric accuracy are few and no one is on Autodesk 123D Catch applied on Architectural Heritage Documentation. Our approach to this challenging problem is to compare the 3Dmodels by Autodesk 123D Catch and 3D models by terrestrial LIDAR considering different object size, from the detail (capitals, moldings, bases) to large scale buildings for practitioner purpose