FAST3DIS: Feed-forward Anchored Scene Transformer for 3D Instance Segmentation
While recent feed-forward 3D reconstruction models provide a strong geometric foundation for scene understanding, extending them to 3D instance segmentation typically relies on a disjointed "lift-and-cluster" paradigm. Grouping dense pixel-wise embeddings via non-differentiable clustering scales poorly with the number of views and disconnects representation learning from the final segmentation objective. In this paper, we present a Feed-forward Anchored Scene Transformer for 3D Instance Segmentation (FAST3DIS), an end-to-end approach that effectively bypasses post-hoc clustering. We introduce a 3D-anchored, query-based Transformer architecture built upon a foundational depth backbone, ada...
| Authors | Changyang Li, Xueqing Huang, Shin-Fang Chng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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