Research Paper#Computer Vision, Depth Estimation, Generative Models🔬 ResearchAnalyzed: Jan 3, 2026 19:47
Visual Autoregressive Depth Estimation
Analysis
This paper introduces a novel approach to monocular depth estimation using visual autoregressive (VAR) priors, offering an alternative to diffusion-based methods. It leverages a text-to-image VAR model and introduces a scale-wise conditional upsampling mechanism. The method's efficiency, requiring only 74K synthetic samples for fine-tuning, and its strong performance, particularly in indoor benchmarks, are noteworthy. The work positions autoregressive priors as a viable generative model family for depth estimation, emphasizing data scalability and adaptability to 3D vision tasks.
Key Takeaways
- •Proposes a novel monocular depth estimation method using visual autoregressive priors.
- •Employs a text-to-image VAR model with a scale-wise conditional upsampling mechanism.
- •Achieves competitive results and state-of-the-art performance in indoor benchmarks.
- •Highlights advantages in data scalability and adaptability to 3D vision tasks.
Reference
“The method achieves state-of-the-art performance in indoor benchmarks under constrained training conditions.”