Zero-P-to-3: Zero-Shot Partial-View Images to 3D Object
Yuxuan Lin, Ruihang Chu, Zhenyu Chen, Xiao Tang, Lei Ke, Haoling Li, Yingji Zhong, Zhihao Li, Shiyong Liu, Xiaofei Wu, Jianzhuang Liu, Yujiu Yang

TL;DR
This paper introduces Zero-P-to-3, a training-free method for reconstructing 3D objects from partial views, effectively generating consistent multi-view images and improving invisible region reconstruction.
Contribution
It proposes a novel fusion-based approach with iterative refinement that aligns priors in DDIM sampling for better partial-view 3D reconstruction without training.
Findings
Outperforms state-of-the-art methods on multiple datasets
Generates coherent multi-view images for invisible regions
Enhances reconstruction quality using geometric structures
Abstract
Generative 3D reconstruction shows strong potential in incomplete observations. While sparse-view and single-image reconstruction are well-researched, partial observation remains underexplored. In this context, dense views are accessible only from a specific angular range, with other perspectives remaining inaccessible. This task presents two main challenges: (i) limited View Range: observations confined to a narrow angular scope prevent effective traditional interpolation techniques that require evenly distributed perspectives. (ii) inconsistent Generation: views created for invisible regions often lack coherence with both visible regions and each other, compromising reconstruction consistency. To address these challenges, we propose \method, a novel training-free approach that integrates the local dense observations and multi-source priors for reconstruction. Our method introduces a…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Generative Adversarial Networks and Image Synthesis
MethodsALIGN
