We propose a monocular depth estimation method based on visual autoregressive (VAR) priors, offering an alternative to diffusion-based approaches. Our method adapts a large-scale text-to-image VAR model and introduces a scale-wise conditional upsampling mechanism with classifier-free guidance. Our approach performs inference in ten fixed autoregressive stages, requiring only 74K synthetic samples for fine-tuning, and achieves competitive results. We report state-of-the-art performance in indoor benchmarks under constrained training conditions, and strong performance when applied to outdoor datasets. This work establishes autoregressive priors as a complementary family of geometry-aware generative models for depth estimation, highlighting advantages in data scalability, and adaptability to 3D vision tasks. Code available at "https://github.com/AmirMaEl/VAR-Depth".
@article{arxiv.2512.22653,
title = {Visual Autoregressive Modelling for Monocular Depth Estimation},
author = {Amir El-Ghoussani and André Kaup and Nassir Navab and Gustavo Carneiro and Vasileios Belagiannis},
journal= {arXiv preprint arXiv:2512.22653},
year = {2025}
}