Fractal Autoregressive Depth Estimation with Continuous Token Diffusion
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 ...
| Authors | Jinchang Zhang, Xinrou Kang, Guoyu Lu |
|---|---|
| Primary category | cs.CV |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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