Deep Prior Learning for Embodied Perception
Embodied systems need geometric perception that exploits available observations beyond images alone. Recent feed-forward 3D models incorporate geometric priors, including camera poses, intrinsics, and depth. However, handling noisy poses, preserving accurate priors, and recovering physical scale require more than simply accepting these inputs. We introduce \emph{Vision-Prior Geometry Grounded Transformer} (VPGGT), a VGGT-based framework that extends OmniVGGT for prior-aware embodied perception. We formulate sensor-motivated pose corruptions from ground-truth trajectories for training and introduce a parameter-free \emph{prior residual connection} (PRC) to mitigate \emph{prior dilution}, w...
| Authors | Yimou Wu, Jiaxin Guo, Yun-hui Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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