Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories.
This work proposes to leverage a pre-trained semantic segmentation network to learn semantically adaptive filters for self-supervised monocular depth estimation. Additionally, a simple two-stage training heuristic is proposed to improve depth estimation performance for dynamic objects that move in a way that induces small apparent motion and thus are projected to infinite depth values when used in an SfM-based supervision framework. Experimental results are shown on the KITTI benchmark, where the approach improves upon the state-of-the-art.
Overview:
+ Good results
+ Doesn't require semantic segmentation ground truth in the monodepth training set
- Not clear if semantic segmentation is needed
- Specific to street scenes
- Experiments only on KITTI
The qualitative results look great and the experiments show that semantic guidance improves quantitative performance by a non-trivial factor. The qualitative results suggest that the results produced with semantic guidance are sharper and more detailed. However, it is not clear that using features from a pre-trained semantic segmentation network is necessary. The proposed technical approach is to use the pixel-adaptive convolutions by Su et. al. to learn content-adaptive filters that are conditioned on the features of the pre-trained semantic segmentation network. These filters could in principle be directly learned from the input images, without needing to first train a semantic segmentation network. The original work by Su et. al. achieved higher detail compared to their baseline by just training the guidance network jointly.  Alternatively, the guidance network could in principle be pre-trained for any other task. The main advantage of the proposed scheme is that the guidance path doesn't need to be trained together with the depth network. On the other hand, unless shown otherwise, we have to assume that the network needs to be pre-trained on some data that is sufficiently close to the indented application domain. This would limit the approach to situations where a reasonable pre-trained semantic segmentation network is available.
The proposed heuristic to filter some dynamic objects is very specific to street scenes and to some degree even to the KITTI dataset. It requires a dominant ground plane and is only able to detect a small subset of dynamic motion (e.g. apparent motion close to zero and object below the horizon). It is also not clear what the actual impact of this procedure is. Section 5.4.2 mentions that Abs. Rel decreases from 0.121 to 0.119, but it is not clear to what this needs to be compared to as there is no baseline in any of the other tables with an Abs. Rel of 0.121. Additionally, while the authors call this a minor decrease, the order of magnitude is comparable to the decrease in error that this method shows over the state-of-the-art (which the authors call statistically significant) and also over the baselines (c.f. Table 2). Can the authors clarify this?
Related to being specific to street scenes: The paper shows experiments only on the KITTI dataset. The apparent requirement to have a reasonable semantic segmentation model available, make it important to evaluate also in other settings (for example on an indoor dataset like NYU) to show that the approach works beyond street scenes (which is one of the in practice not so interesting settings for monocular depth estimation since it is rather easy to just equip cars with additional cameras to solve the depth estimation problem).
Need for a reasonable segmentation model: It is not clear in how far the quality of the segmentation network impacts the quality for the depth estimation task. What about the domain shift where the segmentation model doesn't do so well? Even if the segmentation result is not used directly, the features will still shift. How much would depth performance suffer?
Summary:
While the results look good on a single dataset, I have doubts both about the generality of the proposed approach as well as the need for the specific technical contribution.
=== Post rebuttal update ===
The authors have addressed many of my initial concerns and provided valuable additional experimental evaluations. While I'd like to upgrade my recommendation to weak accept, I strongly encourage the authors to provide additional experiments on different datasets (at least NYU).