Recovering high-quality depth maps from compressed sources has gained significant attention due to the limitations of consumer-grade depth cameras and the bandwidth restrictions during data transmission. However, current methods still suffer from two challenges. First, bit-depth compression produces a uniform depth representation in regions with subtle variations, hindering the recovery of detailed information. Second, densely distributed random noise reduces the accuracy of estimating the global geometric structure of the scene. To address these challenges, we propose a novel framework, termed geometry-decoupled network (GDNet), for compressed depth map super-resolution that decouples the high-quality depth map reconstruction process by handling global and detailed geometric features separately. To be specific, we propose the fine geometry detail encoder (FGDE), which is designed to aggregate fine geometry details in high-resolution low-level image features while simultaneously enriching them with complementary information from low-resolution context-level image features. In addition, we develop the global geometry encoder (GGE) that aims at suppressing noise and extracting global geometric information effectively via constructing compact feature representation in a low-rank space. We conduct experiments on multiple benchmark datasets, demonstrating that our GDNet significantly outperforms current methods in terms of geometric consistency and detail recovery. In the ECCV 2024 AIM Compressed Depth Upsampling Challenge, our solution won the 1st place award. Our codes will be available.

翻译：从压缩源恢复高质量深度图因消费级深度相机的局限性及数据传输中的带宽限制而受到广泛关注。然而，现有方法仍面临两大挑战。首先，位深度压缩在具有细微变化的区域产生均匀的深度表示，阻碍了细节信息的恢复。其次，密集分布的随机噪声降低了场景全局几何结构估计的准确性。为解决这些挑战，我们提出了一种新颖的框架，称为几何解耦网络（GDNet），用于压缩深度图超分辨率，该框架通过分别处理全局与细节几何特征，解耦了高质量深度图的重建过程。具体而言，我们提出了精细几何细节编码器（FGDE），其设计用于聚合高分辨率低层图像特征中的精细几何细节，同时利用来自低分辨率上下文层图像特征的互补信息对其进行增强。此外，我们开发了全局几何编码器（GGE），旨在通过在低秩空间中构建紧凑的特征表示，有效抑制噪声并提取全局几何信息。我们在多个基准数据集上进行了实验，结果表明我们的GDNet在几何一致性和细节恢复方面显著优于现有方法。在ECCV 2024 AIM压缩深度上采样挑战赛中，我们的解决方案荣获第一名。我们的代码将公开提供。