Fractal Autoregressive Depth Estimation with Continuous Token Diffusion

• URL: http://arxiv.org/abs/2603.14702v1
• Date: Mon, 16 Mar 2026 01:19:42 GMT
• Title: Fractal Autoregressive Depth Estimation with Continuous Token Diffusion
• Abstract summary: We propose a Fractal Visual Autoregressive Diffusion framework that reformulates depth estimation as a coarse-to-fine process.<n>A conditional denoising diffusion loss models depth distributions directly in continuous space and mitigates errors caused by discrete quantization.<n>Experiments on standard benchmarks demonstrate strong performance and validate the effectiveness of the proposed design.
• Score: 24.726638033402747
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monocular depth estimation can benefit from autoregressive (AR) generation, but direct AR modeling is hindered by the modality gap between RGB and depth, inefficient pixel-wise generation, and instability in continuous depth prediction. We propose a Fractal Visual Autoregressive Diffusion framework that reformulates depth estimation as a coarse-to-fine, next-scale autoregressive generation process. A VCFR module fuses multi-scale image features with current depth predictions to improve cross-modal conditioning, while a conditional denoising diffusion loss models depth distributions directly in continuous space and mitigates errors caused by discrete quantization. To improve computational efficiency, we organize the scale-wise generators into a fractal recursive architecture, reusing a base visual AR unit in a self-similar hierarchy. We further introduce an uncertainty-aware robust consensus aggregation scheme for multi-sample inference to improve fusion stability and provide a practical pixel-wise reliability estimate. Experiments on standard benchmarks demonstrate strong performance and validate the effectiveness of the proposed design.

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