Self-supervised depth estimation has evolved into an image reconstruction
task that minimizes a photometric loss. While recent methods have made strides
in indoor depth estimation, they often produce inconsistent depth estimation in
textureless areas and unsatisfactory depth discrepancies at object boundaries.
To address these issues, in this work, we propose GAM-Depth, developed upon two
novel components: gradient-aware mask and semantic constraints. The
gradient-aware mask enables adaptive and robust supervision for both key areas
and textureless regions by allocating weights based on gradient magnitudes.The
incorporation of semantic constraints for indoor self-supervised depth
estimation improves depth discrepancies at object boundaries, leveraging a
co-optimization network and proxy semantic labels derived from a pretrained
segmentation model. Experimental studies on three indoor datasets, including
NYUv2, ScanNet, and InteriorNet, show that GAM-Depth outperforms existing
methods and achieves state-of-the-art performance, signifying a meaningful step
forward in indoor depth estimation. Our code will be available at
this https URL

基于自我监督的深度估计方法已经发展成为一种最小化光度损失的图像重建任务。本文提出了 GAM-Depth，它由梯度感知掩码和语义约束两个新颖的组件构成，以解决室内深度估计中纹理缺失区域的深度一致性问题和物体边界处的深度不一致性。通过分配基于梯度大小的权重，梯度感知掩码实现了对关键区域和无纹理区域的自适应和鲁棒的监督。在室内自我监督深度估计中引入语义约束可以改善物体边界处的深度不一致性，利用预训练分割模型的代理语义标签和共优化网络。实验证明，GAM-Depth 在包括 NYUv2、ScanNet 和 InteriorNet 在内的三个室内数据集上优于现有方法，达到了业界领先的性能，是室内深度估计领域的有意义的进展。我们的代码将在此 https URL 上公开。