| title | AnchorComplete: Recovering Complete Scene Meshes from Partial Point Clouds |
|---|---|
| authors | Weirong Chen, Ismail El-Yamany, Ganlin Zhang, Chuanxia Zheng, Andrea Vedaldi, Daniel Cremers |
| date | 2026-11-02 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2611.03228 |
| upvotes | 9 (Hugging Face Daily Papers, 2026-11-06) |
| links | abs pdf hf |
| cite | |
| abstract | Most image-based 3D reconstructors primarily recover visible surfaces, producing partial point clouds with large holes and leaving unobserved geometry unresolved. To address this limitation, we introduce AnchorComplete, a framework that reconstructs complete scene meshes from partial point clouds while preserving observed geometry and supporting scenes of varying scales. Building on TRELLIS.2, our method combines partial-point conditioning, which integrates global scene tokens with local spatial evidence to anchor generation to the input, and metric-scale coarse-to-fine generation, which progressively refines scene geometry within fixed-size windows while maintaining global structural consistency. Experiments on Replica and SCRREAM demonstrate that AnchorComplete consistently outperforms strong baselines in complete-scene reconstruction, particularly under sparse-view settings. |
bibtex
@misc{chen2026anchorcomplete,
title={AnchorComplete: Recovering Complete Scene Meshes from Partial Point Clouds},
author={Weirong Chen and Ismail El-Yamany and Ganlin Zhang and Chuanxia Zheng and Andrea Vedaldi and Daniel Cremers},
year={2026},
eprint={2611.03228},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2611.03228}
}