MVBoost: Boost 3D Reconstruction with Multi-View Refinement
Xiangyu Liu, Xiaomei Zhang, Zhiyuan Ma, Xiangyu Zhu, Zhen Lei

TL;DR
MVBoost introduces a multi-view refinement framework that generates pseudo-ground-truth data from single images, significantly improving 3D reconstruction accuracy and generalization by combining multi-view diffusion and 3D modeling.
Contribution
The paper presents a novel framework that leverages multi-view diffusion and 3D reconstruction to create large-scale training data, enhancing 3D reconstruction performance and generalization.
Findings
Achieves superior 3D reconstruction accuracy
Demonstrates robust generalization across datasets
Provides effective multi-view data generation method
Abstract
Recent advancements in 3D object reconstruction have been remarkable, yet most current 3D models rely heavily on existing 3D datasets. The scarcity of diverse 3D datasets results in limited generalization capabilities of 3D reconstruction models. In this paper, we propose a novel framework for boosting 3D reconstruction with multi-view refinement (MVBoost) by generating pseudo-GT data. The key of MVBoost is combining the advantages of the high accuracy of the multi-view generation model and the consistency of the 3D reconstruction model to create a reliable data source. Specifically, given a single-view input image, we employ a multi-view diffusion model to generate multiple views, followed by a large 3D reconstruction model to produce consistent 3D data. MVBoost then adaptively refines these multi-view images, rendered from the consistent 3D data, to build a large-scale multi-view…
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Taxonomy
Topics3D Surveying and Cultural Heritage · Image Processing and 3D Reconstruction · Computer Graphics and Visualization Techniques
MethodsDiffusion
