- The paper is not particularly novel or exciting since it takes algorithms already applied in the field of semantic segmentation and applies them to the stereo depth estimation problem. The idea of using AutoML for stereo is not particularly novel either, as stated by the authors themselves, even if the proposed algorithm outperforms the previous proposal. - From my point of view the main reason to use AutoML approaches, besides improving raw performances, is extracting hints that can be reused in the design of new network architectures in the future. Unfortunately the authors did not spend much time commenting on these aspects. For example, what might be the biggest takeaways from the found architecture? - I would have liked to see an additional ablation study to better highlight the contribution of the proposed method with respect to AutoDispNet. The main differences with respect to the previously published work is the search performed also on the network level and the use of two separate feature and matching networks. Ablating the contribution of one and the other might have been interesting. - The evaluation on Middleburry should include for fairness a test of the found architecture running at quarter resolution to match the testing setup of all the other deep architecture. While it is true that the ability to run at higher resolution is an advantage of the proposed method there is nothing (besides hw limitation) preventing the other networks to run at higher resolution as well. Therefore I think that a fair comparison between networks running on the same test setup will improve the paper highlighting the contribution of the proposed method. - Some minor implementation details are missing from the paper, I will expand this point in my questions to the authors.
- From my point of view the main reason to use AutoML approaches, besides improving raw performances, is extracting hints that can be reused in the design of new network architectures in the future. Unfortunately the authors did not spend much time commenting on these aspects. For example, what might be the biggest takeaways from the found architecture?