Quick links


Report ID:
July 14, 2016
Download Formats:


Geometry acquisition and processing have become increasingly popular in computer graphics
and vision, with demand for high-quality models driven by advances in 3D printing,
realistic real-time renderings of 3D avatars in video games, digital libraries for historical
objects etc. In this thesis, we focus on techniques to produce and process detailed geometry
including acquisition of real world objects, processing and fusing the captured data, and
synthesizing new surfaces from existing ones.
First, we summarize a 2D acquisition technique called photometric stereo to capture
high resolution surface details. As a validation step, we solve a misalignment problem for
photometric datasets. After the dataset is validated, the surface normals can be computed
using one of the photometric stereo algorithms. In order to decide which algorithm to use,
we present a synthetic photometric benchmark to evaluate various algorithms for different
To produce a detailed surface in 3D, we propose an approach to combine a rough 3D
geometry with detailed normal maps obtained from different views. We begin with unaligned
2D normal maps and a rough 3D geometry, and automatically align each normal map to
the 3D geometry. Next, we map the normals onto the surface, correct and seamlessly blend
them together. We then optimize the geometry to produce a high-quality 3D model.
Next, we introduce a semi-automated system to convert photometric datasets into
geometry-aware non-photorealistic illustrations of surface details that obey the common
conventions of epigraphy (black-and-white archaeological drawings of inscriptions). This
system is composed of rectification of the surface normals to correct camera perspective,
segmentation of the inscriptions from the background, classification of the inscription based
on carving technique, and stylization of the inscriptions in various styles.
Finally, we present an algorithm for realistically transferring surface details (specifically,
displacement maps) from existing high-quality 3D models to simple shapes that may be
created with easy-to-learn modeling tools. Our key insight is to use metric learning to find a
combination of geometric features that successfully predicts detail-map similarities on the
source mesh, and use the learned feature combination to drive the detail transfer.

Follow us: Facebook Twitter Linkedin