This item's files can only be accessed by the Administrator group.
Permalink
https://hdl.handle.net/2142/117609
Description
Title
Convex decomposition of indoor scenes
Author(s)
Vavilala, Vaibhav
Issue Date
2022-12-08
Director of Research (if dissertation) or Advisor (if thesis)
Forsyth, David A
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Deep Learning, TensorFlow, Scene Parsing
Abstract
We describe a method to parse a complex, cluttered indoor scene into primitives which offer a parsimonious abstraction of scene structure. Our primitives are simple convexes. Our method uses a learned regression procedure to predict a parse into a fixed number of convexes from a depth map, but does not require labeled data. The result is then polished with a descent method which adjusts the convexes to produce a very good fit, and greedily removes superfluous primitives.
Because the entire scene is parsed, we can evaluate using traditional depth and normal error metrics. Our evaluation procedure demonstrates that the error in our primitive representation is comparable to that of predicting depth from a single image. We show that our primitives segment the scene well, without ever having seen segmentation labels. Finally, we show that our primitive representation is stable under change of viewpoint.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
