# AnchorComplete: Recovering Complete Scene Meshes from Partial Point Clouds

**Authors:** Weirong Chen, Ismail El-Yamany, Ganlin Zhang, Chuanxia Zheng, Andrea Vedaldi, Daniel Cremers  
**Lab:** Munich Center for Machine Learning · **Published:** 2 Nov 2026 · **Upvotes:** 9  
**Links:** [arXiv](https://arxiv.org/abs/2611.03228) · [PDF](https://arxiv.org/pdf/2611.03228) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.03228.html)

## 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.

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