PRDepth: Pose Refinement Enhancement-Based Monocular Depth Estimation for Indoor Scenes

Indoor depth measurement is widely used in technologies such as virtual reality and augmented reality. However, indoor scenes are typically captured with handheld cameras, resulting in more complex and unpredictable variations between frames. Self-supervised depth estimation relies on frame-to-frame projection for self-constraint, and inaccurate pose predictions between frames significantly hinder depth estimation in indoor environments. To address this issue, PRDepth is proposed, a self-supervised pose refinement method tailored for indoor environments. PRDepth introduces a pose reconstruction iterative module (PRIM) that refines multiframe pose decomposition and reconstruction. By leveraging contextual information from intermediate frames, it mitigates estimation errors caused by large rotations and reduces training errors due to inaccurate rotations, leading to more precise pose predictions between frames. Additionally, to enhance the information exchange and context integration capabilities of the depth network, PRDepth features a depth-weighted incentive module, which includes a global depth enhancement module (GDEM) in the encoder-decoder and a weight-adaptive incentive module (WAIM) in the decoder. The GDEM improves the network’s ability to extract depth information in complex scenes by interacting with global cross-dimensional data. An attention-guided mechanism is adopted by the WAIM to aggregate multiscale feature information and assign adaptive weights to different features, ensuring efficient global context fusion and suppression of redundant information. Experimental results demonstrate that our method significantly outperforms existing state-of-the-art self-supervised monocular depth estimation techniques for indoor scenes. Extensive ablation studies are conducted on each module of PRDepth. PRDepth demonstrates precise depth estimation and robust generalization across indoor datasets, including NYUv2, 7-Scenes, ScanNet, and InteriorNet.