PuriLight: 一种轻量级的洗牌与净化框架用于单目深度估计
PuriLight: A Lightweight Shuffle and Purification Framework for Monocular Depth Estimation
AI总结：
PuriLight通过三种创新模块实现轻量级且结构精确的单目深度估计，提升计算效率与细节保留能力。
摘要：
我们提出了PuriLight，一种轻量级且高效的自监督单目深度估计框架，以解决计算效率和细节保留的双重挑战。尽管最近在自监督深度估计方面的进展减少了对地面真实监督的依赖，但现有方法仍然受到要么笨重的架构影响实用性，要么轻量模型牺牲结构精度的限制。这些双重限制突显了开发轻量但结构精确的架构的迫切需求。我们的框架通过包含三个新模块的三阶段架构来解决这些限制：用于局部特征提取的洗牌-扩张卷积（SDC）模块，用于层次特征增强的旋转适应内核注意力（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。通过有效的协作，这些模块使PuriLight能够实现轻量且准确的特征提取和处理。广泛的实验表明，PuriLight在极小的训练参数下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上提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
