MaLayered

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Comparison of methods to produce a two-layered LDI representation from a single RGB image

One of the major drawbacks of the visualizations used in computer vision is the lack of information about the portion of scene that has been occluded by the foreground objects. Depth maps store the results of a mapping from each pixel to its distance from the camera. Since the pair of RGB image and the depth map store more information than a RGB image itself,they are considered 2.5D. However, a simple depth map fails to alleviate the problem as it stores the values for only the visible part of an image. Unlike human beings who are able to perceive the information even if it has been hidden by confidently extrapolating from what is visible, computer vision models are stymied at only what is immediately visible. This has been resolved with other forms of representations of 2D images, one of which is LDI. However getting better LDI predictions from a single RGB image is challenging and we compare two methods in this work and further experiment with them to see if they they can be made better.

ProjectForm
Title: Comparison of methods to produce a two-layered LDI representation from a single RGB image
Abstract: One of the major drawbacks of the visualizations used in computer vision is the lack of information about the portion of scene that has been occluded by the foreground objects. Depth maps store the results of a mapping from each pixel to its distance from the camera. Since the pair of RGB image and the depth map store more information than a RGB image itself,they are considered 2.5D. However, a simple depth map fails to alleviate the problem as it stores the values for only the visible part of an image. Unlike human beings who are able to perceive the information even if it has been hidden by confidently extrapolating from what is visible, computer vision models are stymied at only what is immediately visible. This has been resolved with other forms of representations of 2D images, one of which is LDI. However getting better LDI predictions from a single RGB image is challenging and we compare two methods in this work and further experiment with them to see if they they can be made better.
Student: Richa Mishra
Director: Federico Tombari
Supervisor: Helisa Dhamo
Type: Bachelor Thesis
Area: Machine Learning, Computer Vision
Status: finished
Start: 30.08.2018
Finish: 30.11.2018
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Edit | Attach | Refresh | Diffs | More | Revision r1.1 - 02 May 2019 - 14:33 - HelisaDhamo