[Submitted on 22 Jul 2026]
Title:Axolotl3D: a Unified Framework for Faithful 3D Shape Completion
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Abstract:Recent 3D generative models produce high-quality geometry from a single image using large-scale priors and diffusion architectures. However, they assume complete visibility and single-view inputs, limiting applicability in multi-view, occluded, or editing scenarios. Although prior works address these challenges individually, they lack a unified framework for controllable 3D completion under diverse conditioning signals. We present Axolotl3D, a multi-modal and occlusion-aware 3D generation model that jointly conditions on images, visibility masks, camera parameters, and a partial point cloud. The point cloud serves as a geometric anchor promoting faithful shape completion, while camera parameters ensure consistent multi-view alignment in a shared 3D coordinate system. A unified training strategy synthesizes diverse conditioning regimes from large-scale 3D data, enabling robust cross-modal reasoning. Experiments on Toys4K and OmniObject3D demonstrate state-of-the-art performance under both clean and occluded settings, as well as strong results in real-world reconstruction and geometry-consistent editing.
Comments: Accepted to ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.20660 [cs.CV]
(or arXiv:2607.20660v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.20660
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Anita Hu [view email] [v1] Wed, 22 Jul 2026 18:38:20 UTC (28,240 KB)
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