We propose PuriLight, a lightweight and efficient framework for self-supervised monocular depth estimation, to address the dual challenges of computational efficiency and detail preservation. While recent advances in self-supervised depth estimation have reduced reliance on ground truth supervision, existing approaches remain constrained by either bulky architectures compromising practicality or lightweight models sacrificing structural precision. These dual limitations underscore the critical need to develop lightweight yet structurally precise architectures. Our framework addresses these limitations through a three-stage architecture incorporating three novel modules: the Shuffle-Dilation Convolution (SDC) module for local feature extraction, the Rotation-Adaptive Kernel Attention (RAKA) module for hierarchical feature enhancement, and the Deep Frequency Signal Purification (DFSP) module for global feature purification. Through effective collaboration, these modules enable PuriLight to achieve both lightweight and accurate feature extraction and processing. Extensive experiments demonstrate that PuriLight achieves state-of-the-art performance with minimal training parameters while maintaining exceptional computational efficiency. Codes will be available at https://github.com/ishrouder/PuriLight.

翻译：我们提出PuriLight，一种用于自监督单目深度估计的轻量高效框架，以应对计算效率与细节保持的双重挑战。尽管自监督深度估计的最新进展减少了对真实值监督的依赖，现有方法仍受限于要么是笨重的架构损害实用性，要么是轻量级模型牺牲结构精度。这些双重局限凸显了开发轻量且结构精确的架构的迫切需求。我们的框架通过包含三个新颖模块的三阶段架构来解决这些局限：用于局部特征提取的混洗-空洞卷积（SDC）模块、用于层次特征增强的旋转自适应核注意力（RAKA）模块，以及用于全局特征净化的深度频率信号净化（DFSP）模块。通过有效协作，这些模块使PuriLight能够同时实现轻量且精确的特征提取与处理。大量实验表明，PuriLight以最少的训练参数实现了最先进的性能，同时保持了卓越的计算效率。代码将在 https://github.com/ishrouder/PuriLight 提供。