The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


"3D MODELING FROM PHOTOMETRY AND GEOMETRY"

By

Mr. Ping Tan


Abstract

In this thesis, we focus on the 3D reconstruction from multiple images. We
have explored different approaches for reconstruction including
photometric methods, i.e. modeling from a changing lighting, and geometric
methods, i.e. modeling from a changing viewpoint.

Photometric stereo uses images from different lighting conditions to build
3D model of an object. In this work, we improve photometric stereo from
both reconstruction accuracy and data capturing simplicity. In
conventional photometric stereo algorithms, surface shape can only be
recovered at the resolution of input images, since only one normal
direction is computed for each pixel. However, for a rough surface, there
often exists sub-pixel level geometry structures. We have studied the
relationship between surface reflectance and sub-pixel geometry structures
to design a new photometric stereo algorithm that recovers sub-pixel level
geometry structures. Our method significantly improves the modeling
accuracy. Another limitation in conventional photometric stereo is that,
the lighting conditions have to be recorded in data capturing. Otherwise,
surface shape can only be recovered up to an unknown General Bas-Relief
(GBR) shape ambiguity. In this thesis, we have observed that
isotropy/reciprocity induces symmetry structures on Gauss sphere for any
isotropic surface. And these symmetry structures are destroyed by a GBR
transformation. Hence, we can resolve the unknown GBR shape ambiguity by
restoring the broken symmetry for any isotropic surface. With our method,
there is no need to record lighting conditions in data capturing for
isotropic surface. This makes the data capturing procedure significantly
simplified.

Image-based modeling (IBM) uses images from different viewpoints to build
3D model of an object. Previous methods on image-based modeling are very
successful at recovering camera poses and a set of 3D points of the
object. But these recovered 3D points are typically unstructured. To
generate 3D models ready to be used in applications such as movies, games
or virtual tours, we organize these unstructured 3D points and build high
quality texture mapped models from them. There are two key contributions
in our approach. Firstly, we propose to segment 3D points together with 2D
images into individual objects by a joint 2D-3D segmentation method.
General segmentation of images is a very difficult problem. But since we
have both 2D and 3D information which reinforce each other, the
segmentation can be performed quite efficiently. Segmentation results
provide clearly defined object boundary which is very useful for high
quality modeling. Secondly, we proposed to synthesize occluded structure
according to object priors learned from visible structures. An inevitable
problem in image-based method is occlusion. No or less information is
available in occluded regions. Our method can successfully propagate
information from visible region to invisible region. Our method is tested
on image-based modeling of trees. With our approach, we can build highly
realistic digital model of trees from their images.


Date:			Thursday, 30 August 2007

Time:			10:00a.m.-12:00noon

Venue:			Room 3402
			Lifts 17-18

Chairman:		Prof. Wei Ping LI (MATH)

Committee Members:	Prof. Long Quan (Supervisor)
			Prof. Huamin Qu
			Prof. Chiew-Lan Tai
			Prof. Zexiang Li (ECE)
			Prof. Stephen Lin (Microsoft Research Asia)
			Prof. Hung-Tat Tsui (Elec. Engg., CUHK)


**** ALL are Welcome ****