Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach

We present a new approach for modelling and rendering existing architectural scenes from a sparse set of still photographs. Our modelling approach, which combines both geometry-based and imagebased techniques, has two components. The first component is a photogrammetricmodellingmethodwhich facilitates the recovery of the basic geometry of the photographed scene. Our photogrammetric modelling approach is effective, convenient, and robust because it exploits the constraints that are characteristic of architectural scenes. The second component is a model-based stereo algorithm, which recovers how the real scene deviates from the basic model. By making use of the model, our stereo technique robustly recovers accurate depth from widely-spaced image pairs. Consequently, our approach canmodel large architectural environmentswith far fewer photographs than current image-based modelling approaches. For producing renderings, we present view-dependent texture mapping, a method of compositing multiple views of a scene that better simulates geometric detail on basic models. Our approach can be used to recover models for use in either geometry-based or image-based rendering systems. We present results that demonstrate our approach's ability to create realistic renderings of architectural scenes from viewpoints far from the original photographs

Enhancement of Visual Realism with BRDF for Patient Specific Bronchoscopy Simulation

Abstract. This paper presents a novel method for photorealistic rendering of the bronchial lumen by directly deriving matched shading and texture parameters from video bronchoscope images. 2D/3D registration is used to match video bronchoscope images with 3D CT scan of the same patient, such that patient specific modelling and simulation with improved visual realism can be achieved. With the proposed method, shading parameters are recovered by modelling the bidirectional reflectance distribution function (BRDF) of the visible surfaces by exploiting the restricted lighting configurations imposed by the bronchoscope. The derived BRDF is then used to predict the expected shading intensity such that a texture map independent of lighting conditions can be extracted. This allows the generation of new views not captured in the original bronchoscopy video, thus allowing free navigation of the acquired 3D model with enhanced photo-realism.

Modelling of 3D Objects Using Unconstrained and Uncalibrated Images Taken with a Handheld Camera

Abstract. 3D models are an essential part of computer graphics applications such as games, movie special effects, urban and landscape design, architecture, virtual heritage, visual impact studies, and virtual environments such as Second Life. We have developed a novel technique which allows the construction of 3D models using image sequences acquired by a handheld low-cost digital camera. In contrast to alternative technologies, such as laser scanners, structured lighting, and sets of calibrated cameras, our approach can be used by everyone having access to a consumer-level camera. The user only has to create a set of images from different view directions, input them into our algorithm, and a 3D model is returned. We use a novel combination of advanced computer vision algorithms for feature detection, feature matching, and projection matrix estimation in order to reconstruct a 3D point cloud representing the location of geometric features estimated from input images. In a second step a full 3D model is reconstructed using the projection matrix and a triangulation process. We tested our algorithm using a variety of data sets of objects of different scales acquired under different weather and lighting conditions. The results show that our algorithm is stable and enables inexperienced users to easily create complex 3D content using a simple consumer level camera.