# Efficient Camera Pose Augmentation for View Generalization in Robotic Policy Learning

arXiv: https://arxiv.org/abs/2603.29192 · HTML: https://mlsanity.com/paper/2603.29192/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2026.

## Built on by (most cited first, 2 of 2)

- [UniviewVLA: A Unified Multiview Vision-Language-Action Model with World Modeling](https://mlsanity.com/paper/2606.21501/) (2026)
- [3DROID: A Renderable 3D Gaussian Dataset with Measured Per-Scene Reliability](https://mlsanity.com/paper/2610.01744/) (2026)

## Abstract (arXiv)

Prevailing 2D-centric visuomotor policies exhibit a pronounced deficiency in novel view generalization, as their reliance on static observations hinders consistent action mapping across unseen views. In response, we introduce GenSplat, a feed-forward 3D Gaussian Splatting framework that facilitates view-generalized policy learning through novel view rendering. GenSplat employs a permutation-equivariant architecture to reconstruct high-fidelity 3D scenes from sparse, uncalibrated inputs in a single forward pass. To ensure structural integrity, we design a 3D-prior distillation strategy that regularizes the 3DGS optimization, preventing the geometric collapse typical of purely photometric supervision. By rendering diverse synthetic views from these stable 3D representations, we systematically augment the observational manifold during training. This augmentation forces the policy to ground its decisions in underlying 3D structures, thereby ensuring robust execution under severe spatial perturbations where baselines severely degrade.