Novel View Synthesis from Casually Recorded Videos

Generating new, photorealistic views of a scene given only a single video is a difficult task that computer vision researchers have worked on for decades. This problem has recently seen a resurgence in interest due to its potential application in areas such as virtual reality. However, current novel...

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Bibliographic Details
Main Author: Qian, Eric Ding
Other Authors: Freeman, William T.
Format: Thesis
Published: Massachusetts Institute of Technology 2022
Online Access:https://hdl.handle.net/1721.1/140103
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author Qian, Eric Ding
author2 Freeman, William T.
author_facet Freeman, William T.
Qian, Eric Ding
author_sort Qian, Eric Ding
collection MIT
description Generating new, photorealistic views of a scene given only a single video is a difficult task that computer vision researchers have worked on for decades. This problem has recently seen a resurgence in interest due to its potential application in areas such as virtual reality. However, current novel view synthesis techniques are not suitable for the short, casual videos that people typically record. Such videos deviate from the setups that these approaches typically use, where there are dense, high-resolution images of the scene. In this paper, we propose a method for refining an initial, coarse scene geometry which we then use for novel view synthesis on short video sequences. The core of our method is a geometry refinement step where we project the geometry to source views to remove inconsistent points. This refined geometry provides important shape and appearance information in data poor regions that would otherwise be difficult to accurately render. We evaluate our approach on the RealEstate10K dataset and demonstrate that compared to prior work, we synthesize views that are more temporally consistent.
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spelling mit-1721.1/1401032022-02-08T04:07:08Z Novel View Synthesis from Casually Recorded Videos Qian, Eric Ding Freeman, William T. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Generating new, photorealistic views of a scene given only a single video is a difficult task that computer vision researchers have worked on for decades. This problem has recently seen a resurgence in interest due to its potential application in areas such as virtual reality. However, current novel view synthesis techniques are not suitable for the short, casual videos that people typically record. Such videos deviate from the setups that these approaches typically use, where there are dense, high-resolution images of the scene. In this paper, we propose a method for refining an initial, coarse scene geometry which we then use for novel view synthesis on short video sequences. The core of our method is a geometry refinement step where we project the geometry to source views to remove inconsistent points. This refined geometry provides important shape and appearance information in data poor regions that would otherwise be difficult to accurately render. We evaluate our approach on the RealEstate10K dataset and demonstrate that compared to prior work, we synthesize views that are more temporally consistent. M.Eng. 2022-02-07T15:24:15Z 2022-02-07T15:24:15Z 2021-09 2021-11-03T19:25:28.352Z Thesis https://hdl.handle.net/1721.1/140103 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology
spellingShingle Qian, Eric Ding
Novel View Synthesis from Casually Recorded Videos
title Novel View Synthesis from Casually Recorded Videos
title_full Novel View Synthesis from Casually Recorded Videos
title_fullStr Novel View Synthesis from Casually Recorded Videos
title_full_unstemmed Novel View Synthesis from Casually Recorded Videos
title_short Novel View Synthesis from Casually Recorded Videos
title_sort novel view synthesis from casually recorded videos
url https://hdl.handle.net/1721.1/140103
work_keys_str_mv AT qianericding novelviewsynthesisfromcasuallyrecordedvideos