Doctoral Thesis Oral Defense - Nicole Feng Traffic21 Classroom, Gates Hillman 6501 and Zoom Anonymous (not verified) Tue, 04/07/2026 - 18:10 In Person and Virtual - ET
Algorithms for Generalized Signed Distance and Winding Numbers
NICOLE FENG In this talk, I'll discuss algorithms for generalized inside/outside computation (via winding numbers ) and signed distance computation. By "generalized", I mean that these algorithms make geometric inferences from imperfect data comprising incomplete, inaccurate, or ambiguous observations or representations of shapes. In other words, these algorithms generalize from imperfect data and implicitly approximate the true underlying curve or surface. A theme is that generalization can often be achieved by processing globally-defined functions encoding the geometry of interest, rather than the original, defective curve or surface. For both inside/outside and signed distance computation we can unlock further control over geometry and topology by processing higher-order derivatives of these functions. Another theme is that inside/outside and signed distance computation are closely related problems; towards this end, we provide a formalization of their relationship that justifies the design of our algorithms. Thesis Committee Keenan Crane (Chair) Nancy Pollard Ioannis Gkioulekas Chris Wojtan (Institute of Science and Technology Austria) In Person and Zoom Participation. See announcement. April 15, 2026 9:00am April 15, 2026 10:30am https://nzfeng.github.io/
Ph.D. Candidate Computer Science Department Carnegie Mellon University
Thesis Oral matthewstewart@cmu.edu Nicole Feng Graphics Traffic21 Classroom, Gates Hillman 6501 and Zoom Speaker: NICOLE FENG, Ph.D. Candidate Computer Science Department Carnegie Mellon University Talk Title: Algorithms for Generalized Signed Distance and Winding Numbers In this talk, I'll discuss algorithms for generalized inside/outside computation (via winding numbers) and signed distance computation. By "generalized", I mean that these algorithms make geometric inferences from imperfect data comprising incomplete, inaccurate, or ambiguous observations or representations of shapes. In other words, these algorithms generalize from imperfect data and implicitly approximate the true underlying curve or surface. A theme is that generalization can often be achieved by processing globally-defined functions encoding the geometry of interest, rather than the original, defective curve or surface. For both inside/outside and signed distance computation we can unlock further control over geometry and topology by processing higher-order derivatives of these functions. Another theme is that inside/outside and signed distance computation are closely related problems; towards this end, we provide a formalization of their relationship that justifies the design of our algorithms. Thesis Committee Keenan Crane (Chair) Nancy Pollard Ioannis Gkioulekas Chris Wojtan (Institute of Science and Technology Austria) In Person and Zoom Participation. See announcement. Computer Science Department (CSD)