Monocular depth estimation using Convolutional Neural Networks (CNNs) has
shown impressive performance in outdoor driving scenes. However,
self-supervised learning of indoor depth from monocular sequences is quite
challenging for researchers because of the following two main reasons. One is
the large areas of low-texture regions and the other is the complex ego-motion
on indoor training datasets. In this work, our proposed method, named
IndoorDepth, consists of two innovations. In particular, we first propose a
novel photometric loss with improved structural similarity (SSIM) function to
tackle the challenge from low-texture regions. Moreover, in order to further
mitigate the issue of inaccurate ego-motion prediction, multiple photometric
losses at different stages are used to train a deeper pose network with two
residual pose blocks. Subsequent ablation study can validate the effectiveness
of each new idea. Experiments on the NYUv2 benchmark demonstrate that our
IndoorDepth outperforms the previous state-of-the-art methods by a large
margin. In addition, we also validate the generalization ability of our method
on ScanNet dataset. Code is availabe at this https URL

室内深度自动估计的自我监督学习是困难的，挑战主要集中在大面积低纹理区域和复杂的内部训练数据上。本文提出了一种名为 IndoorDepth 的新方法，包括两个创新点：首先，我们提出了一种改进的结构相似（SSIM）功能的新颖光度损失函数，用于应对低纹理区域的挑战；此外，为了进一步减轻不准确的自身运动预测问题，我们使用多个不同阶段的光度损失进行训练，以构建更深的姿态网络。通过对实验的深入研究，我们验证了每个新想法的有效性。在 NYUv2 基准测试中，我们的 IndoorDepth 方法在性能上超过了之前的最新方法。此外，我们还在 ScanNet 数据集上验证了我们的方法的泛化能力。