SR-JEPA: Learning Predictive Latent State in 3D Scenes
Joint-embedding predictive architectures learn by predicting latent representations of missing observations, yet many masked JEPAs are evaluated primarily through the encoders they produce. We ask what a trained predictive pathway itself infers when an entire entity is absent from a native 3D scene. We introduce SR-JEPA, a point-native JEPA for scene-scale point clouds whose original frozen predictive pathway can be queried at a supplied location. At evaluation, every point of one object is removed before encoding and replaced by the same shape-free 32-point query at its centroid. Training uses only self-contained 3D EMA targets: no reconstruction, semantic labels, language, or lifted 2D ...
| Authors | Zihan Zhou, Qifu Wen, Xi Zeng |
|---|---|
| Primary category | cs.CV |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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