通过双维度约束提升模糊聚焦的深度估计精度
DualFocus: Depth from Focus with Spatio-Focal Dual Variational Constraints
- 利用空间与焦距双重梯度模式建模聚焦变化
- 在四个数据集上均超越现有最佳方法
- 适合处理纹理复杂或深度突变的场景
基于焦点的深度（DFF）通过分析不同焦距下图像堆栈中的聚焦线索实现精确深度估计。尽管基于学习的方法已取得进展，但在具有精细纹理或深度突变的复杂场景中，聚焦线索常变得模糊或误导。本文提出DualFocus，一种新颖的DFF框架，利用焦距堆栈因聚焦变化产生的独特梯度模式，联合建模空间与焦距维度上的聚焦变化。该方法引入变分公式，包含针对DFF设计的双约束：空间约束利用不同焦距下的梯度模式变化，区分真实深度边缘与纹理伪影；焦距约束强制聚焦概率呈单峰且单调，符合物理聚焦规律。这些归纳偏置提升了复杂区域的鲁棒性与准确性。在四个公开数据集上的全面实验表明，DualFocus在深度精度和感知质量方面均持续优于当前最优方法。
原文摘要 · Abstract (English)
Depth-from-Focus (DFF) enables precise depth estimation by analyzing focus cues across a stack of images captured at varying focal lengths. While recent learning-based approaches have advanced this field, they often struggle in complex scenes with fine textures or abrupt depth changes, where focus cues may become ambiguous or misleading. We present DualFocus, a novel DFF framework that leverages the focal stack's unique gradient patterns induced by focus variation, jointly modeling focus changes over spatial and focal dimensions. Our approach introduces a variational formulation with dual constraints tailored to DFF: spatial constraints exploit gradient pattern changes across focus levels to distinguish true depth edges from texture artifacts, while focal constraints enforce unimodal, monotonic focus probabilities aligned with physical focus behavior. These inductive biases improve robustness and accuracy in challenging regions. Comprehensive experiments on four public datasets demonstrate that DualFocus consistently outperforms state-of-the-art methods in both depth accuracy and perceptual quality.
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