From Few to Full: High-Resolution 3D Object Reconstruction from Sparse Views and Unknown Poses
dc.contributor.author | Yao, Grekou | en_US |
dc.contributor.author | Mavromatis, Sebastien | en_US |
dc.contributor.author | Mari, Jean-Luc | en_US |
dc.contributor.editor | Liu, Lingjie | en_US |
dc.contributor.editor | Averkiou, Melinos | en_US |
dc.date.accessioned | 2024-04-30T08:21:16Z | |
dc.date.available | 2024-04-30T08:21:16Z | |
dc.date.issued | 2024 | |
dc.description.abstract | Recent progress in 3D reconstruction has been driven by generative models, moving from traditional multi-view dependence to single-image diffusion model based techniques. However, these innovative approaches often face challenges with sparse view scenarios, requiring known poses or template shapes, often failing in high-resolution reconstructions. Addressing these issues, we introduce the ''F2F'' (Few to Full) framework, designed for crafting high-resolution 3D models from few views and unknown camera poses, creating fully realistic 3D objects without external constraints. F2F employs a hybrid approach, optimizing both implicit and explicit representations through a unique pipeline involving a pretrained diffusion model for pose estimation, a deformable tetrahedra grid for feature volume construction, and an MLP (neural network) for surface optimization. Our method sets a new standard by ensuring surface geometry, topology, and semantic consistency through differentiable rendering, aiming for a comprehensive solution in 3D reconstruction from sparse views. | en_US |
dc.description.sectionheaders | Posters | |
dc.description.seriesinformation | Eurographics 2024 - Posters | |
dc.identifier.doi | 10.2312/egp.20241045 | |
dc.identifier.isbn | 978-3-03868-239-4 | |
dc.identifier.issn | 1017-4656 | |
dc.identifier.pages | 2 pages | |
dc.identifier.uri | https://doi.org/10.2312/egp.20241045 | |
dc.identifier.uri | https://diglib.eg.org/handle/10.2312/egp20241045 | |
dc.publisher | The Eurographics Association | en_US |
dc.rights | Attribution 4.0 International License | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | CCS Concepts: Computing methodologies → Sparse views; 3D reconstruction; Hybrid 3D representation; Differentiable rendering | |
dc.subject | Computing methodologies → Sparse views | |
dc.subject | 3D reconstruction | |
dc.subject | Hybrid 3D representation | |
dc.subject | Differentiable rendering | |
dc.title | From Few to Full: High-Resolution 3D Object Reconstruction from Sparse Views and Unknown Poses | en_US |
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