Reconstruction of Three Dimensional Models from Real Images

Nowadays computer graphics allows to render realistic images of the 3D world. However, before such images can be generated, graphics models of the world must be available. Traditionally, these models where obtained using 3D modelling packages. This is a very time consuming process and the achievable level of detail and realism is limited. Therefore, more and more 3D models are obtained by sampling the world. Different approaches are available for this. Best known are probably laser range scanners and other devices that project light onto the object and obtain 3D information by observing the reflection. Another approach consists of using images of the scene or objects one wishes to reconstruct. A simple example consists of taking a picture to use it as a texture map for a 3D model. However, not only the appearance, but also the 3D shape can be extracted from images. This will be the main subject of this paper.

IEEE Signal Processing Magazine, 2000

T here exist several goals for three-dimensional (3-D) digitization and modeling of natural and cultural heritage objects, such as for accurate detailed documentation, digital preservation and restoration, physical replicas, virtual tourism, and research or education. We focus on the detailed documentation aspect. Capturing the 3-D data requires a technique that is highly accurate; portable due to accessibility problems; low cost due to limited budgets; fast due to the usually short allowed time on the site so as not to disturb works or visitors; and flexible and scalable due to the wide variety and sizes of sites and heritage objects. It is also important that the technique captures dense 3-D data on the necessary surface elements to guarantee a realistic experience close up or to monitor surface condition over time. Furthermore, the fine geometric details are needed because even with rich texture maps, models without these details will exhibit too smooth and flat-looking surfaces or polygonized silhouettes that are easily detected by the human eye.

2011

This paper presents an automated methodology for the accurate reconstruction of surfaces and 3D objects. This method was developed for close-range photogrammetric applications, with a particular attention to terrestrial free-form objects that can be modeled with point clouds extracted from high resolution images. The image orientation phase is carried out using an automatic procedure based on a rigorous bundle adjustment. Then, an approximate model is obtained using a patch-based matching strategy. Then, this initial model is refined using a Multi-Image Least Squares Matching (MGCM) implementation, increasing in this way the precision of the final model and giving the possibility to evaluate its consistency using a rigorous variance-covariance propagation. The multi-image matching result is a dense point cloud describing in an accurate way the object surface. The derived point cloud is used to obtain a mesh which is finally textured obtaining a photo-realistic result.

Geomatics and Environmental Engineering, 2021

This research attempted to determine the optimal photo overlap, number of con‐ trol points and method of camera calibration for a photogrammetric 3D model reconstruction of an object of cultural heritage value. Terrestrial images of the object were taken with a hand‐held digital camera and processed in the Con‐ textCapture software using the Structure‐from‐Motion (SfM) algorithm. A total station was used to measure ground control points (GCPs) and check points. Here, the research workflow, methodology, and various analyses concerning different configurations of the aforementioned factors are described. An at‐ tempt to assess the parameters which should be implemented in order to pro‐ vide a high degree of accuracy of the model and reduce time‐consumption both during fieldwork and data processing was taken. The manuscript discusses the results of the analyses and compares them with other studies presented by dif‐ ferent authors and indicates further potential directions of studies wi...

Abstract. In this paper a system is presented which automatically extracts a 3D surface model from a sequence of photographs of a static scene. The system can deal with unknown camerasettings. In addition the parameters of this camera are allowed to change during acquisition (eg by zooming or focussing). No prior knowledge about the scene is necessary to build the 3D models. This system therefore offers a high degree of flexibility. The system is based on state-of-the-art algorithms recently developed in computer vision.

Lecture Notes in Computer Science, 2001

In a previous paper we described a system which recursively recovers a super-resolved three dimensional surface model from a set of images of the surface. In that paper we assumed that the camera calibration for each image was known. In this paper we solve two problems. Firstly, if an estimate of the surface is already knob-n, the problem is to calibrate a new image relative to the existing surface model. Secondly, if no surface estimate is available, the relative camera calibration between the images in the set must be estimated. This will allow an initial surface model to be estimated. Results of both types of estimation are given.

Sensors

Advances in the scientific fields of photogrammetry and computer vision have led to the development of automated multi-image methods that solve the problem of 3D reconstruction. Simultaneously, 3D scanners have become a common source of data acquisition for 3D modeling of real objects/scenes/human bodies. This article presents a comprehensive overview of different 3D modeling technologies that may be used to generate 3D reconstructions of outer or inner surfaces of different kinds of targets. In this context, it covers the topics of 3D modeling using images via different methods, it provides a detailed classification of 3D scanners by additionally presenting the basic operating principles of each type of scanner, and it discusses the problem of generating 3D models from scans. Finally, it outlines some applications of 3D modeling, beyond well-established topographic ones.

International Journal of Computer Vision, 2009

This article presents a novel method for acquiring high-quality solid models of complex 3D shapes from multiple calibrated photographs. After the purely geometric constraints associated with the silhouettes found in each image have been used to construct a coarse surface approximation in the form of a visual hull, photoconsistency constraints are enforced in three consecutive steps: (1) the rims where the surface grazes the visual hull are first identified through dynamic programming; (2) with the rims now fixed, the visual hull is carved using graph cuts to globally optimize the photoconsistency of the surface and recover its main features; (3) an iterative (local) refinement step is finally used to recover fine surface details. The proposed approach has been implemented, and experiments with seven real data sets are presented, along with qualitative and quantitative comparisons with several state-of-the-art image-based-modeling algorithms.

A system for the automatic reconstruction of real world objects from multiple uncalibrated camera views is presented. The camera position and orientation for all views, the 3-D shape of the rigid object as well as associated color information are recovered from the image sequence. The system proceeds in four steps. First, the internal camera parameters describing the imaging geometry of the camera are calibrated using a reference object. Second, an initial 3-D description of the object is computed from two views. This model information is then used in a third step to estimate the camera positions for all available views using a novel linear 3-D motion and shape estimation algorithm. The main feature of this third step is the simultaneous estimation of 3-D camera motion parameters and object shape refinement with respect to the initial 3-D model. The initial 3-D shape model exhibits only a few degrees of freedom and the object shape refinement is defined as flexible deformation of the initial shape model. Our formulation of the shape deformation allows the object texture to slide on the surface, which differs from traditional flexible body modeling. This novel combined shape and motion estimation using sliding texture considerably improves the calibration data of the individual views in comparison to fixed-shape model-based camera motion estimation. Since the shape model used for model-based camera motion estimation is approximate only, a volumetric 3-D reconstruction process is initiated in the fourth step that combines the information from all views simultaneously. The recovered object consists of a set of voxels with associated color information that describe even fine structures and details of the object. New views of the object can be rendered from the recovered 3-D model, which has potential applications in virtual reality or multimedia systems and the emerging field of video coding using 3-D scene models.

Proceedings Geometric Modeling and Processing 2000. Theory and Applications, 2000

This paper describes a system for rapid acquisition of 3D models of objects for use in applications such as CAD and VR. The system consists of an image capturing platform, which comprises a PC-controlled turntable and CCD camera, and associated computer vision and graphics algorithms for 3D model acquisition. An algorithm for accurately recovering 3D points from image sequences is described. Experiments performed on synthetic and real data show that the algorithm is accurate in recovering the coordinates of 3D points and is robust against noise in 2D feature location and 3D object rotation. Results of applying the system to acquiring 3D models of real objects are illustrated.