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1 Introduction

Recent years have witnessed a rise in the availability of fast, accurate range scanners. These range scanners have provided data for applications such as medicine, reverse engineering, and digital film-making. Many of these devices generate range images; i.e., they produce depth values on a regular sampling lattice. Figure 1 illustrates how an optical triangulation scanner can be used to acquire a range image. By connecting nearest neighbors with triangular elements, one can construct a range surface as shown in Figure 1d. Range images are typically formed by sweeping a 1D or 2D sensor linearly across an object or circularly around it, and generally do not contain enough information to reconstruct the entire object being scanned. Accordingly, we require algorithms that can merge multiple range images into a single description of the surface. A set of desirable properties for such a surface reconstruction algorithm includes:

In this paper, we present a volumetric method for integrating range images that possesses all of these properties. In the next section, we review some previous work in the area of surface reconstruction. In section 3, we describe the core of our volumetric algorithm. In section 4, we show how this algorithm can be used to fill gaps in the reconstruction using knowledge about the emptiness of space. Next, in section 5, we describe how we implemented our volumetric approach so as to keep time and space costs reasonable. In section 6, we show the results of surface reconstruction from many range images of complex objects. Finally, in section 7 we conclude and discuss limitations and future directions.

  figure82
Figure 1: From optical triangulation to a range surface. (a) In 2D, a narrow laser beam illuminates a surface, and a linear sensor images the reflection from an object. The center of the image pulse maps to the center of the laser, yielding a range value. The uncertainty, tex2html_wrap_inline723 , in determining the center of the pulse results in range uncertainty, tex2html_wrap_inline725 along the laser's line of sight. When using the spacetime analysis for optical triangulation [6], the uncertainties run along the lines of sight of the CCD. (b) In 3D, a laser stripe triangulation scanner first spreads the laser beam into a sheet of light with a cylindrical lens. The CCD observes the reflected stripe from which a depth profile is computed. The object sweeps through the field of view, yielding a range image. Other scanner configurations rotate the object to obtain a cylindrical scan or sweep a laser beam or stripe over a stationary object. (c) A range image obtained from the scanner in (b) is a collection of points with regular spacing. (d) By connecting nearest neighbors with triangles, we create a piecewise linear range surface. 


next up previous
Next: 2 Previous work Up: A Volumetric Method Previous: A Volumetric Method