Kinect cloud normals: towards surface orientation estimation
Rungta, Pratik S.
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https://hdl.handle.net/2142/26108
Description
Title
Kinect cloud normals: towards surface orientation estimation
Author(s)
Rungta, Pratik S.
Issue Date
2011-08-25T22:14:25Z
Director of Research (if dissertation) or Advisor (if thesis)
Hoiem, Derek W.
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)
microsoft kinect
dataset
point cloud
normal estimation
Abstract
Since the early days of Computer Vision, we have explored what is possible in the realm of ‘Scene Understanding’. The advent of consumer-grade RGBD cameras has broadened the possibilities within this realm. The data they provide is able to serve as ground truth information or training data for a class of algorithms, which would otherwise be extremely difficult, if not impossible, to train. This thesis serves the purpose of gathering data from such a source, specifically, it demonstrates how to collect a dual pair of depth and RGB images of a multitude of scenes and an approach to determine surface normals from these images.
The goal of this endeavor is to provide a dataset of RGB images and surface normal estimates for each image so that the latter may serve as the ground truth for both training and evaluation of algorithms estimating surface normals from the RGB image alone.
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