Deeper into Self-Supervised Monocular Indoor Depth Estimation
Chao Fan Zhenyu Yin Yue Li Feiqing Zhang
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 https://github.com/fcntes/IndoorDepth.