ARDepth shifts the paradigm of monocular depth estimation (MDE) from the dominant diffusion-based denoising approach to a structured auto-regressive (AR) framework. While diffusion models treat depth as a globally smooth field refined through iterative denoising, ARDepth views geometric structure as a hierarchical composition. It generates depth representations progressively, starting from coarse spatial layouts and increasing in resolution to capture fine-grained local details.
Key Innovations
To support this generative process, the authors introduce two primary mechanisms:
Scale-Progressive Conditioning (SPC): This module extracts multi-scale visual features from the input image and injects them at each specific generation stage. This ensures that the model uses global layout cues for coarse stages and high-frequency visual evidence for fine-grained details, improving spatial alignment.
Semantic-Aware Guidance (SAG): This module incorporates scene-level semantic priors derived from multi-modal language models. These priors help the model maintain global structural consistency and reduce ambiguity in textureless or occluded regions.
Why It Matters
Standard diffusion-based MDE models often struggle to preserve sharp discontinuities, thin structures, and accurate near-field geometry because uniform denoising does not explicitly account for the hierarchical nature of scene geometry. By reformulating MDE as a coarse-to-fine auto-regressive task, ARDepth achieves superior structural consistency and sharper boundaries. This work demonstrates that auto-regressive generation is a highly competitive and scalable alternative to diffusion for dense geometric prediction, offering a more natural way to model the piecewise and scale-dependent organization of real-world scenes.
The task of predicting a dense depth map representing the distance from the camera to scene points using only a single RGB image.
Auto-regressive (AR) Generation
A generative process where data is constructed sequentially, with each new part of the output conditioned on the previously generated components.
Scale-Progressive Conditioning (SPC)
A mechanism that injects multi-scale visual features into the model at each stage of the auto-regressive generation process to align visual evidence with the current resolution.
Semantic-Aware Guidance (SAG)
A module that provides high-level scene descriptions or semantic priors to help the model maintain global structural coherence during the depth generation process.
Hierarchical VAR Transformer
The core architecture used by ARDepth to predict depth residuals in a coarse-to-fine sequence, leveraging the structure of Visual Auto-Regressive (VAR) models.
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