Visual Autoregressive Modelling for Monocular Depth Estimation

• URL: http://arxiv.org/abs/2512.22653v1
• Date: Sat, 27 Dec 2025 17:08:03 GMT
• Title: Visual Autoregressive Modelling for Monocular Depth Estimation
• Authors: Amir El-Ghoussani, André Kaup, Nassir Navab, Gustavo Carneiro, Vasileios Belagiannis,
• Abstract summary: We propose a monocular depth estimation method based on visual autoregressive ( VAR) priors.<n>Our method adapts a large-scale text-to-image VAR model and introduces a scale-wise conditional upsampling mechanism.<n>We report state-of-the-art performance in indoor benchmarks under constrained training conditions, and strong performance when applied to outdoor datasets.
• Score: 69.01449528371916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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".

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