Inovis: Instant Novel-View Synthesis

Harrer M, Franke L, Fink L, Stamminger M, Weyrich T (2023)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2023

Conference Proceedings Title: ACM SIGGRAPH Asia 2023 Conference Proceedings

Event location: Sydney

URI: https://reality.tf.fau.de/publications/2023/harrerfranke2023inovis

DOI: 10.1145/3610548.3618216

Abstract

Novel-view synthesis is an ill-posed problem in that it requires inference of previously unseen information. Recently, reviving the traditional field of image-based rendering, neural methods proved particularly suitable for this interpolation/extrapolation task; however, they often require a-priori scene-completeness or costly preprocessing steps and generally suffer from long (scene-specific) training times. Our work draws from recent progress in neural spatio-temporal supersampling to enhance a state-of-the-art neural renderer's ability to infer novel-view information at inference time. We adapt a supersampling architecture [Xiao et al. 2020], which resamples previously rendered frames, to instead recombine nearby camera images in a multi-view dataset. These input frames are warped into a joint target frame, guided by the most recent (point-based) scene representation, followed by neural interpolation. The resulting architecture gains sufficient robustness to significantly improve transferability to previously unseen datasets. In particular, this enables novel applications for neural rendering where dynamically streamed content is directly incorporated in a (neural) image-based reconstruction of a scene. As we will show, our method reaches state-of-the-art performance when compared to previous works that rely on static and sufficiently densely sampled scenes; in addition, we demonstrate our system's particular suitability for dynamically streamed content, where our approach is able to produce high-fidelity novel-view synthesis even with significantly fewer available frames than competing neural methods.

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How to cite

APA:

Harrer, M., Franke, L., Fink, L., Stamminger, M., & Weyrich, T. (2023). Inovis: Instant Novel-View Synthesis. In Association for Computing Machinery (Eds.), ACM SIGGRAPH Asia 2023 Conference Proceedings. Sydney, AU.

MLA:

Harrer, Mathias, et al. "Inovis: Instant Novel-View Synthesis." Proceedings of the SIGGRAPH Asia 2023 Sydney, Sydney Ed. Association for Computing Machinery, 2023.

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