A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties. Our volumetric representation consists of a cumulative weighted signed distance function. Working with one range image at a time, we first scan-convert it to a distance function, then combine this with the data already acquired using a simple additive scheme. To achieve space efficiency, we employ a run-length encoding of the volume. To achieve time efficiency, we resample the range image to align with the voxel grid and traverse the range and voxel scanlines synchronously. We generate the f...

We advocate the use of point sets to represent shapes. We provide a definition of a smooth manifold surface from a set of points close to the original surface. The definition is based on local maps from differential geometry, which are approximated by the method of moving least squares (MLS). We present tools to increase or decrease the density of the points, thus, allowing an adjustment of the spacing among the points to control the fidelity of the representation. To display the point set surface, we introduce a novel point rendering technique. The idea is to evaluate the local maps according to the image resolution. This results in high quality shading effects and smooth silhouettes at interactive frame rates.

We advocate the use of point sets to represent shapes. We provide a definition of a smooth manifold surface from a set of points close to the original surface. The definition is based on local maps from differential geometry, which are approximated by the method of moving least squares (MLS). The computation of points on the surface is local, which results in an out-of-core technique that can handle any point set.

This paper deals with robust registration of object views in the presence of uncertainties and noise in depth data. Errors in registration of multiple views of a 3D object severely affect view integration during automatic construction of object models. We derive a minimum variance estimator (MVE) for computing the view transformation parameters accurately from range data of two views of a 3D object. The results of our experiments show that view transformation estimates obtained using MVE are significantly more accurate than those computed with an unweighted error criterion for registration. Key words: Image registration, view transformation estimation, view integration, automatic object modeling, 3D free-form objects, range data. 1 Introduction An important issue in the design of 3D object recognition systems is building models of physical objects. Object models are extensively used for synthesizing and predicting object appearances from desired viewpoints and also for recognizing th...

We present a volumetric method that can efficiently create triangular meshes from 3-D geometric data. This data can be presented in the form of images, profiles or unordered points. The mesh model can be created at different resolutions and can also be closed to make a true volumetric model.

Errors in registration of multiple views of an object based on estimated transformations between views can affect surface classification. We derive a minimum variance estimator (MVE) for computing the transformation parameters accurately from range data of two different views of a 3D object. The results of our experiments show that the solution obtained using MVE is significantly more reliable than the estimate obtained with an unweighted distance criterion for registration. 1 Introduction Automatic model construction of 3D objects involves the following three steps: (i) data acquisition, (ii) registration of different views, and (iii) integration [1]. In this paper the term "data" refers to range data that can be obtained using a laser range scanner. Integration of multiple views requires knowledge of the transformation relating the data obtained from multiple views. The goal of registration, also known as the correspondence problem, is to compute the transformations that relate mul...

The use of detailed geometric models is a critical factor for achieving realism in most computer graphics applications. In the past few years, we have observed an increasing demand for faithful representations of real scenes, primarily driven by applications whose goal is to create extremely realistic experiences by building virtual replicas of real environments. Potential uses of this technology include entertainment, training and simulation, special effects, forensic analysis, and remote walkthroughs. Creating models of real scenes is, however, a complex task for which the use of traditional modeling techniques is inappropriate. Aiming to simplify the modeling and rendering tasks, several image-based techniques have been proposed in recent years. Among these, the combined use of laser rangefinders and color images appears as one the most promising approaches due to its relative independence of the sampled geometry and short acquisition time. Renderings of scenes modeled with such a technique can potentially exhibit an unprecedented degree of photorealism. But before one can actually render new views of these virtualized environments, several challenges need to be addressed. This tutorial provides an overview of the main issues associated with the modeling and rendering of real environments sampled with laser rangefinders, and discusses the main techniques used to address these challenges.

A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties. Our volumetric representation consists of a cumulative weighted signed distance function. Working with one range image at a time, we first scan-convert it to a distance function, then combine this with the data already acquired using a simple additive scheme. To achieve space efficiency, we employ a run-length encoding of the volume. To achieve time efficiency, we resample the range image to align with the voxel grid and traverse the range and voxel scanlines synchronously. We generate the final manifold by extracting an isosurface from the volumetric grid. We show that under certain assumptions, this isosurface is optimal in the least squares sense. To fill gaps in the model, we tessellate over the boundaries between regions seen to be empty and regions never observed. Using this method, we are able to integrate a large number of range images (as many as 70) yielding seamless, high-detail models of up to 2.6 million triangles. CR Categories: I.3.5 [Computer Graphics] Computational Geometry and Object Modeling Additional keywords: Surface fitting, three-dimensional shape recovery, range image integration, isosurface extraction 1