Perceiving Shape from Surface Contours via Artificial Neural Networks
Name
Brandt-lebrandt-SM-EECS-2021-thesis.pdf
Description
Thesis PDF
Size
5.03 MB
Format
Adobe PDF
Checksum (MD5)
790117c64db90a09614b16e06c49cfc9
Author(s)
Brandt, Laura E.
Advisor(s)
Freeman, William T
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
This thesis explores the challenge of teaching a machine how to perceive shape from surface contour markings. Such markings are commonly used in clothing, data visualizations, and other man-made constructs, because humans have an apparently natural ability to interpret them. By glancing at a simple collection of curves drawn upon a 3D surface, we can quickly glean general shape and curvature information; and such contours drawn on a 2D surface can give the illusion of curvature where there is none. Machines have no such visual intuition, and therefore are not particularly well-equipped to interpret things designed to leverage this human ability. We approach this problem by synthesizing a new dataset of surface grid- and line- marked 3D surfaces (SurfaceGrid) and training a deep neural net to estimate their shape. Our algorithm successfully reconstructs shape from synthetic 3D surfaces rendered with a variety of grid- and line-contour markings with < 0.5% mean-squared relative error, and extracts general shape and curvature information from 2D pictures of 3D mesh models and real-world wireframe objects.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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