提升远距离单目深度估计精度，解决细节丢失与数据分布偏差问题

Monocular depth estimation (MDE) aims to infer per-pixel depth from a single RGB image. While diffusion models have advanced MDE with impressive generalization, they often exhibit limitations in accurately reconstructing far-range regions. This difficulty arises from two key challenges. First, the implicit multi-scale processing in standard spatial-domain models can be insufficient for preserving the fine-grained, high-frequency details crucial for distant structures. Second, the intrinsic long-tail distribution of depth data imposes a strong training bias towards more prevalent near-range regions. To address these, we propose VistaDepth, a novel diffusion framework designed for balanced and accurate depth perception. We introduce two key innovations. First, the Latent Frequency Modulation (LFM) module enhances the model's ability to represent high-frequency details. It operates by having a lightweight network predict a dynamic, content-aware spectral filter to refine latent features, thereby improving the reconstruction of distant structures. Second, our BiasMap mechanism introduces an adaptive reweighting of the diffusion loss strategically scaled across diffusion timesteps. It further aligns the supervision with the progressive denoising process, establishing a more consistent learning signal. As a result, it mitigates data bias without sacrificing training stability. Experiments show that VistaDepth achieves state-of-the-art performance for diffusion-based MDE, particularly excelling in reconstructing detailed and accurate depth in far-range regions.