From Few to Full: High-Resolution 3D Object Reconstruction from Sparse Views and Unknown Poses
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
CCS Concepts: Computing methodologies → Sparse views; 3D reconstruction; Hybrid 3D representation; Differentiable rendering
@inproceedings{10.2312:egp.20241045,
booktitle = {Eurographics 2024 - Posters},
editor = {Liu, Lingjie and Averkiou, Melinos},
title = {{From Few to Full: High-Resolution 3D Object Reconstruction from Sparse Views and Unknown Poses}},
author = {Yao, Grekou and Mavromatis, Sebastien and Mari, Jean-Luc},
year = {2024},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-239-4},
DOI = {10.2312/egp.20241045}
}