Deeper into Self-Supervised Monocular Indoor Depth Estimation
This work addresses the problem of accurate indoor depth estimation for researchers and applications in robotics or AR/VR, but it appears incremental as it builds on existing self-supervised methods with specific improvements.
The paper tackles self-supervised monocular depth estimation in indoor scenes, which is challenging due to low-texture regions and complex ego-motion, and reports that their method outperforms previous state-of-the-art by a large margin on the NYUv2 benchmark.
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.