ScaleBlind: Point Cloud Completion under Unknown Scale
Point cloud completion aims to infer a complete 3D shape from a partial point cloud and serves as a fundamental building block for downstream tasks such as reconstruction, editing, and simulation. Despite the recent progress, existing learning-based methods often implicitly rely on access to the ground-truth shape scale (GT-scale) during both training- and testing-time normalization, assuming privileged information that is unavailable in real-world inference. This hidden assumption limits practical deployment and can lead to severe completion artifacts, e.g., over- or under-completion and nested shells, once the oracle GT-scale cue is removed. We observe that the recent foundation image g...
| Authors | Shenghui Wu, Chen Wang, Yuan Feng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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