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In this paper, we explore an intriguing\nscenario for view synthesis: extrapolating views from imagery captured by\nnarrow-baseline stereo cameras, including VR cameras and now-widespread\ndual-lens camera phones. We call this problem stereo magnification, and propose\na learning framework that leverages a new layered representation that we call\nmultiplane images (MPIs). Our method also uses a massive new data source for\nlearning view extrapolation: online videos on YouTube. Using data mined from\nsuch videos, we train a deep network that predicts an MPI from an input stereo\nimage pair. This inferred MPI can then be used to synthesize a range of novel\nviews of the scene, including views that extrapolate significantly beyond the\ninput baseline. We show that our method compares favorably with several recent\nview synthesis methods, and demonstrate applications in magnifying\nnarrow-baseline stereo images.","url_abs":"http://arxiv.org/abs/1805.09817v1","url_pdf":"http://arxiv.org/pdf/1805.09817v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stereo-magnification-learning-view-synthesis","repo_url":"https://github.com/google/stereo-magnification","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"novel-view-synthesis","task_name":"Novel View Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.09817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.09817"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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