Sequential view synthesis with transformer
Nguyen-Ha, Phong; Huynh, Lam; Rahtu, Esa; Heikkilä, Janne (2021-02-25)
Nguyen-Ha P., Huynh L., Rahtu E., Heikkilä J. (2021) Sequential View Synthesis with Transformer. In: Ishikawa H., Liu CL., Pajdla T., Shi J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science, vol 12625. Springer, Cham. https://doi.org/10.1007/978-3-030-69538-5_42
© Springer Nature Switzerland AG 2021. This is a post-peer-review, pre-copyedit version of an article published in Computer Vision – ACCV 2020. ACCV 2020. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-69538-5_42.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2021051229663
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Abstract
This paper addresses the problem of novel view synthesis by means of neural rendering, where we are interested in predicting the novel view at an arbitrary camera pose based on a given set of input images from other viewpoints. Using the known query pose and input poses, we create an ordered set of observations that leads to the target view. Thus, the problem of single novel view synthesis is reformulated as a sequential view prediction task. In this paper, the proposed Transformer-based Generative Query Network (T-GQN) extends the neural-rendering methods by adding two new concepts. First, we use multi-view attention learning between context images to obtain multiple implicit scene representations. Second, we introduce a sequential rendering decoder to predict an image sequence, including the target view, based on the learned representations. Finally, we evaluate our model on various challenging datasets and demonstrate that our model not only gives consistent predictions but also doesn’t require any retraining for finetuning.
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