将深度估计从像素域转到频域，分步预测低频到高频成分，提升精度。
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain
- 在离散余弦域中分频段估计深度，利用频率分解建模局部相关性
- 先预测低频成分建立全局结构，再逐步细化高频细节，实现渐进式重建
- 在NYU-Depth-V2等3个数据集上达到当前最优，适合追求高精度单目深度的场景
本文提出DCDepth，一种全新的单目深度估计框架。不同于传统的空间域像素级估计，该方法将深度图块变换至离散余弦域后，直接估计其频率系数。这一独特形式使模型能捕捉每个图块内的局部深度相关性。关键在于，频率变换将深度信息解耦为不同频段：低频成分蕴含核心场景结构，高频成分刻画精细细节。基于此，我们设计渐进策略——先预测低频成分以建立全局场景上下文，再逐级预测更高频成分以优化局部细节。我们在NYU-Depth-V2、TOFDC和KITTI三个数据集上进行了全面实验，验证了DCDepth的领先性能。代码已开源：https://github.com/w2kun/DCDepth。
原文摘要 · Abstract (English)
In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial domain, our approach estimates the frequency coefficients of depth patches after transforming them into the discrete cosine domain. This unique formulation allows for the modeling of local depth correlations within each patch. Crucially, the frequency transformation segregates the depth information into various frequency components, with low-frequency components encapsulating the core scene structure and high-frequency components detailing the finer aspects. This decomposition forms the basis of our progressive strategy, which begins with the prediction of low-frequency components to establish a global scene context, followed by successive refinement of local details through the prediction of higher-frequency components. We conduct comprehensive experiments on NYU-Depth-V2, TOFDC, and KITTI datasets, and demonstrate the state-of-the-art performance of DCDepth. Code is available at https://github.com/w2kun/DCDepth.
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