Utonia: Toward One Encoder for All Point Clouds
We dream of a future where point clouds from all domains can come together to shape a single model that benefits them all. Toward this goal, we present Utonia, a first step toward training a single self-supervised point transformer encoder across diverse domains, spanning remote sensing, outdoor LiDAR, indoor RGB-D sequences, object-centric CAD models, and point clouds lifted from RGB-only videos. Despite their distinct sensing geometries, densities, and priors, Utonia learns a consistent representation space that transfers across domains. This unification improves perception capability while revealing intriguing emergent behaviors that arise only when domains are trained jointly. Beyond ...
| Authors | Yujia Zhang, Xiaoyang Wu, Yunhan Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 March 2026 |
| Last updated at source | 3 July 2026 |
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