DinoComplete: 3D Shape Completion with Distilled Semantic Priors and State Space Models
3D shape completion from partial scans remains challenging for unseen categories and noisy real-world observations, where geometry alone is often insufficient for inferring missing structure. We present DinoComplete, a deterministic and efficient shape completion framework that augments geometric reconstruction with voxel-aligned semantic priors distilled from DINO features. First, we construct multi-view DINO feature volumes aligned with ShapeNet data and train a student network to predict dense semantic features directly from incomplete shapes. These predicted features capture global structure and part-aware semantic context while remaining aligned with the underlying geometry. We then ...
| Authors | Furkan Mert Algan, Eckehard Steinbach |
|---|---|
| Primary category | cs.CV |
| Published | 26 May 2026 |
| Last updated at source | 21 September 2026 |
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