Learning Stable Canonical Worlds for Novel View Synthesis and Beyond
Feed-forward Gaussian splatting (FFGS) facilitates real-time novel view synthesis, yet current methods often remain tied to view-dependent predictions. As more input views are added, they may accumulate noisy or redundant evidence instead of converging to a stable scene representation. In this paper, we introduce CanonicalGS, a feed-forward pipeline that maps cluttered multi-view observations into a stable, scene-centric representation. CanonicalGS first extracts view-centric evidence from depth, semantic features, and uncertainty estimates, and then aggregates this evidence in a canonical latent world using uncertainty-aware fusion. By emphasizing reliable observations while suppressing ...
| Authors | Xiaoyu Xu, Jian Zou, Sheyang Tang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 June 2026 |
| Last updated at source | 25 June 2026 |
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