Depth estimation from a single image is of paramount importance in the realm of computer vision, with a multitude of applications. Conventional methods suffer from the trade-off between consistency and fine-grained details due to the local-receptive field limiting their practicality. This lack of long-range dependency inherently comes from the convolutional neural network part of the architecture. In this paper, a dual window transformer-based network, namely DwinFormer, is proposed, which utilizes both local and global features for end-to-end monocular depth estimation. The DwinFormer consists of dual window self-attention and cross-attention transformers, Dwin-SAT and Dwin-CAT, respectively. The Dwin-SAT seamlessly extracts intricate, locally aware features while concurrently capturing global context. It harnesses the power of local and global window attention to adeptly capture both short-range and long-range dependencies, obviating the need for complex and computationally expensive operations, such as attention masking or window shifting. Moreover, Dwin-SAT introduces inductive biases which provide desirable properties, such as translational equvariance and less dependence on large-scale data. Furthermore, conventional decoding methods often rely on skip connections which may result in semantic discrepancies and a lack of global context when fusing encoder and decoder features. In contrast, the Dwin-CAT employs both local and global window cross-attention to seamlessly fuse encoder and decoder features with both fine-grained local and contextually aware global information, effectively amending semantic gap. Empirical evidence obtained through extensive experimentation on the NYU-Depth-V2 and KITTI datasets demonstrates the superiority of the proposed method, consistently outperforming existing approaches across both indoor and outdoor environments.

翻译：单张图像的深度估计在计算机视觉领域具有至关重要的意义，有着广泛的应用。传统方法由于局部感受野的限制，在一致性与精细细节之间存在权衡，从而限制了其实用性。这种长距离依赖的缺失本质上源于架构中的卷积神经网络部分。本文提出了一种基于双窗Transformer的网络——DwinFormer，它利用局部和全局特征进行端到端的单目深度估计。DwinFormer由双窗自注意力Transformer（Dwin-SAT）和双窗交叉注意力Transformer（Dwin-CAT）组成。Dwin-SAT在无缝提取复杂局部感知特征的同时，能够捕获全局上下文。它利用局部和全局窗口注意力的能力，巧妙捕获短距离和长距离依赖，无需复杂且计算开销大的操作（如注意力掩码或窗口移位）。此外，Dwin-SAT引入了归纳偏置，提供了诸如平移等变性和对大规模数据依赖性较低等理想特性。同时，传统解码方法通常依赖跳跃连接，在融合编码器和解码器特征时可能导致语义差异和全局上下文缺失。相比之下，Dwin-CAT采用局部和全局窗口交叉注意力，将精细的局部信息与上下文感知的全局信息无缝融合到编码器和解码器特征中，有效弥合了语义差距。在NYU-Depth-V2和KITTI数据集上进行的大量实验获得的经验证据表明，所提方法具有优越性，在室内和室外环境下均持续优于现有方法。