Depth assisted full resolution network for single image-based view synthesis
Cun, Xiaodong1; Pun, Chi-Man1; Xu, Feng2; Gao, Hao3
Conference NameACM SIGGRAPH 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2018
Source PublicationACM SIGGRAPH 2018 Posters, SIGGRAPH 2018
Conference Date8 12, 2018 - 8 16, 2018
Conference PlaceVancouver, BC, Canada
Author of SourceAssociation for Computing Machinery, Inc
AbstractSynthesizing images of novel viewpoints is widely investigated in computer vision and graphics. Most works in this topic focus on using multi-view images to synthesize viewpoints in-between. In this paper, we consider extrapolation, and we take a step further to do extrapolation from one single input image. This task is very challenging for two major reasons. First, some parts of the scene may not be observed in the input viewpoint but are required for novel ones. Second, 3D information is lacking for single view input but is crucial to determine pixel movements between viewpoints. Although very challenging, we observe that human brains are always able to imagine novel viewpoints. The reason is that human brains have learned in our daily lives to understand the depth order of objects in a scene [Chen et al. 2016] and infer what the scene looks like when viewing from another viewpoint. © 2018 Copyright held by the owner/author(s).
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Document TypeConference paper
Affiliation1.University of Macau, Japan;
2.Tsinghua University, Japan;
3.Nanjing University of Posts and Telecommunications, Japan
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Cun, Xiaodong,Pun, Chi-Man,Xu, Feng,et al. Depth assisted full resolution network for single image-based view synthesis[C]//Association for Computing Machinery, Inc,2018.
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