VistaDepth: Improving far-range Depth Estimation with Spectral Modulation and Adaptive Reweighting

Monocular depth estimation infers per-pixel depth from a single RGB image. It remains particularly challenging in far-range regions, where sparse observations and long-tailed depth distributions bias learning toward near-range content. Diffusion models offer a promising alternative to discriminative foundation models by leveraging rich generative priors for zero-shot generalization. However, existing methods still struggle to recover fine distant structures under weak supervision and limited image evidence. To address this, we propose VistaDepth, a diffusion framework for far-range depth estimation. It improves far-range prediction through two complementary mechanisms: Latent Frequency Modulation (LFM), which refines latent features via dynamic, content-aware spectral filtering, and BiasMap, which adaptively reweights the diffusion objective toward distant, structurally informative regions while remaining aligned with denoising. Extensive experiments on five benchmarks show that VistaDepth achieves state-of-the-art performance among diffusion-based methods. Despite training on orders of magnitude less data than discriminative baselines, it remains competitive globally and excels in challenging far-range regions.