Shape Estimation under General Reflectance and Transparency

In recent years there has been significant progress in increasing the scope, accuracy and flexibility of 3D photography methods. However there are still significant open problems where complex optical properties of mirroring or transparent objects cause many assumptions of traditional algorithms to break down. In this work we present three approaches that attempt to deal with some of these challenges using a few camera views and simple illumination. First, we consider the problem of reconstructing the 3D position and surface normal of points on a time-varying refractive surface. We show that two viewpoints are sufficient to solve this problem in the general case, even if the refractive index is unknown. We introduce a novel ``stereo matching'' criterion called refractive disparity, appropriate for refractive scenes, and develop an optimization-based algorithm for individually reconstructing the position and normal of each point projecting to a pixel in the input views. Second, we present a new method for reconstructing the exterior surface of a complex transparent scene with inhomogeneous interior. We capture images from each viewpoint while moving a proximal light source to a 2D or 3D set of positions giving a 2D (or 3D) dataset per pixel, called the scatter-trace. The key is that while light transport within a transparent scene's interior can be exceedingly complex, a pixel's scatter trace has a highly-constrained geometry that reveals the direct surface reflection, and leads to a simple ``Scatter-trace stereo'' algorithm for computing the exterior surface geometry. Finally, we develop a reconstruction system for scenes with reflectance properties ranging from diffuse to specular. We capture images of the scene as it is illuminated by a planar, spatially non-uniform light source. Then we show that if the source is translated to a parallel position away from the scene, a particular scene point integrates a magnified region of light from the plane. We observe this magnification at each pixel and show how it relates to the source-relative depth of the surface. Next we show how calibration relating the camera and source planes allows for robustness to specular objects and recovery of 3D surface points.

Department dc:contributor.department

Computer Science

Year dc:date.issued

2011

Author dc:creator

• Morris, Nigel Jed Wesley

Advisor dc:contributor.advisor

• Kutulakos, Kiriakos Neoklis

• Computer Vision
• 3D reconstruction
• Transparency
• Stereo
• Multi-view
• Dynamic
• Specular

Statement dc:rights

• Attribution-ShareAlike 3.0 Unported

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-sa/3.0/

Language dc:language.iso

en_ca

Handle dc:identifier.uri

http://hdl.handle.net/1807/29819

OAI identifier oai:identifier

oai:utoronto.scholaris.ca:1807/29819