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

General Dynamic Scene Reconstruction from Multiple View Video

This paper introduces a general approach to dynamic scene reconstruction from multiple moving cameras without prior knowledge or limiting constraints on the scene structure, appearance, or illumination. Existing techniques for dynamic scene reconstruction from multiple wide-baseline camera views primarily focus on accurate reconstruction in controlled environments, where the cameras are fixed and calibrated and background is known. These approaches are not robust for general dynamic scenes captured with sparse moving cameras. Previous approaches for outdoor dynamic scene reconstruction assume prior knowledge of the static background appearance and structure. The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras. Evaluation is performed on a variety of indoor and outdoor scenes with cluttered backgrounds and multiple dynamic non-rigid objects such as people. Comparison with state-of-the-art approaches demonstrates improved accuracy in both multiple view segmentation and dense reconstruction. The proposed approach also eliminates the requirement for prior knowledge of scene structure and appearance

3D scanning of cultural heritage with consumer depth cameras

Three dimensional reconstruction of cultural heritage objects is an expensive and time-consuming process. Recent consumer real-time depth acquisition devices, like Microsoft Kinect, allow very fast and simple acquisition of 3D views. However 3D scanning with such devices is a challenging task due to the limited accuracy and reliability of the acquired data. This paper introduces a 3D reconstruction pipeline suited to use consumer depth cameras as hand-held scanners for cultural heritage objects. Several new contributions have been made to achieve this result. They include an ad-hoc filtering scheme that exploits the model of the error on the acquired data and a novel algorithm for the extraction of salient points exploiting both depth and color data. Then the salient points are used within a modified version of the ICP algorithm that exploits both geometry and color distances to precisely align the views even when geometry information is not sufficient to constrain the registration. The proposed method, although applicable to generic scenes, has been tuned to the acquisition of sculptures and in this connection its performance is rather interesting as the experimental results indicate

3D Object Reconstruction using Multi-View Calibrated Images

In this study, two models are proposed, one is a visual hull model and another one is a 3D object reconstruction model. The proposed visual hull model, which is based on bounding edge representation, obtains high time performance which makes it to be one of the best methods. The main contribution of the proposed visual hull model is to provide bounding surfaces over the bounding edges, which results a complete triangular surface mesh. Moreover, the proposed visual hull model can be computed over the camera networks distributedly. The second model is a depth map based 3D object reconstruction model which results a watertight triangular surface mesh. The proposed model produces the result with acceptable accuracy as well as high completeness, only using stereo matching and triangulation. The contribution of this model is to playing with the 3D points to find the best reliable ones and fitting a surface over them

Robot Assisted 3D Shape Acquisition Optical Systems

In this chapter, a short description of the basic concepts about optical methods for the acquisition of three-dimensional shapes is first presented. Then two applications of the surface reconstruction are presented: the passive technique Shape from Silhouettes and the active technique Laser Triangolation. With both these techniques the sensors (telecameras and laser beam) were moved and oriented by means of a robot arm. In fact, for complex objects, it is important that the measuring device can move along arbitrary paths and make its measurements from suitable directions. This chapter shows how a standard industrial robot with a laser profile scanner can be used to achieve the desired d-o-f. Finally some experimental results of shape acquisition by means of the Laser Triangolation technique are reported