Probabilistic Inverse Cameras: Image to 3D via Multiview Geometry

• URL: http://arxiv.org/abs/2412.10273v1
• Date: Fri, 13 Dec 2024 16:46:46 GMT
• Title: Probabilistic Inverse Cameras: Image to 3D via Multiview Geometry
• Authors: Rishabh Kabra, Drew A. Hudson, Sjoerd van Steenkiste, Joao Carreira, Niloy J. Mitra,
• Abstract summary: We introduce a hierarchical probabilistic approach to go from a 2D image to multiview 3D.<n>A diffusion "prior" models the unseen 3D geometry, which then conditions a diffusion "decoder" to generate novel views of the subject.
• Score: 37.52243979087041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a hierarchical probabilistic approach to go from a 2D image to multiview 3D: a diffusion "prior" models the unseen 3D geometry, which then conditions a diffusion "decoder" to generate novel views of the subject. We use a pointmap-based geometric representation in a multiview image format to coordinate the generation of multiple target views simultaneously. We facilitate correspondence between views by assuming fixed target camera poses relative to the source camera, and constructing a predictable distribution of geometric features per target. Our modular, geometry-driven approach to novel-view synthesis (called "unPIC") beats SoTA baselines such as CAT3D and One-2-3-45 on held-out objects from ObjaverseXL, as well as real-world objects ranging from Google Scanned Objects, Amazon Berkeley Objects, to the Digital Twin Catalog.

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