{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/139532"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/139532","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Perceiving Shape from Surface Contours via Artificial Neural Networks","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.","abstract_html":"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 &lt; 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.","abstract_has_math":false,"creators":["Brandt, Laura E."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Freeman, William T"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-06","date_published":"2021-06","updated_at":"2026-07-22T22:20:47Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/139532","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Freeman, William T"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Perceiving Shape from Surface Contours via Artificial Neural Networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Freeman, William T"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Brandt, Laura E."],"dc:date.accessioned":["2022-01-14T15:18:02Z"],"dc:date.available":["2022-01-14T15:18:02Z"],"dc:date.issued":["2021-06"],"dc:description.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."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/139532"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Perceiving Shape from Surface Contours via Artificial Neural Networks"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:20:47Z"}