Free space exploration from a single RGB image

Issaranon, Theerasit

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https://hdl.handle.net/2142/115735 Copy

Description

Title

Free space exploration from a single RGB image

Author(s)

Issaranon, Theerasit

Issue Date

2022-04-22

Director of Research (if dissertation) or Advisor (if thesis)

Forsyth, David

Doctoral Committee Chair(s)

Forsyth, David

Committee Member(s)

• Hoiem, Derek
• Hasegawa-Johnson, Mark
• Gupta, Saurabh

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Occupancy Prediction
• Single-Image
• Space
• Voxels
• Depth

Abstract

From a single RGB image, we can infer many properties of the scene. One of them is depth. It is a well-established problem, and we are able to produce a result reliably. In our case, we are interested in the free space in the scene, whether it is visible or invisible in the RGB image. This dissertation reports the construction of network that is able to produce a high-quality free space map from a single RGB image. This is an interesting problem that minimal current research has explored. We also investigate several strategies to achieve this goal. From our experiments, we conclude the voxel occupancy map is the most suitable because it is the most flexible and a natural extension of a pixel depth map. Utilizing 3D convolution and separating features for depth and voxel yield the best result. With several techniques that we have experimented with, the result is an improvement of about 20% on the convolutional network baseline. Producing an occupancy map by using only an RGB is an interesting topic with many potential applications. Therefore, this topic is worth investigating in the future.

Graduation Semester

2022-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Theerasit Issaranon

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