3D Photography

Goals

3D scanning is the inverse of computer-aided manufacturing --- given a physical object, such as a clay model of a car, a turbine blade, or a chair, create a digital representation capturing its shape, color, reflectance, etc. 3D scanning is similar in principle to other technologies (like photocopying and video) that quickly, accurately, and cheaply record useful aspects of physical reality, producing electronic representations that can be used in ways physical objects cannot - i.e., viewed on CRTs, edited, stored in databases, transmitted over networks, analyzed in computer simulations, and used as templates for making physical copies. We have developed algorithms that construct geometric models from the data produced by current 3D scanners. For example, the left image below shows a set of datapoints captured from a multiple viewpoint laser scan of a distributor cap. The image on the right shows the surface reconstructed by our software. For more information on our surface representation, see our page on subdivision surfaces.

The next step is to record a color (rgb) value for each data point during the scanning process, and use those to create colored surface models. However, "color" is a much more complex property. Real objects are not lambertian reflectors --- they look different depending on the direction from which they are viewed. Shiny surfaces show specular highlights, and some materials, like velvet, are inherently anisotropic. To achive more realistic appearance it is necessary to model and render the luminance (or radiance). Luminance is a 4D function assigning an rgb value to each surface point and viewing direction. By representing luminance as a function over the surface of an object, we obtain a "surface light field." The overall goal of this research is modeling surface light fields and rendering them at interactive rates. The image pair below consists of renderings from different viewpoints of a reconstructed surface light field of a porcelain fish with rapid variations in diffuse and specular reflection:

Personnel

Current PIs: Brian Curless (CSE), Tom Duchamp (Mathematics) and Werner Stuetzle (Statistics).
Current students:

Daniel Wood (CSE) and Greg Arden (Mathematics)

Collaborators: David Salesin (CSE) and Steven Seitz (CSE)
Previous PIs: Tony DeRose and John McDonald
Previous students: Hugues Hoppe,  Kari Pulli, Wyvern (Ken) Aldinger, and Daniel Azuma

Support

This work has been supported by the following grants:

Publications

Other materials