1. I think the innovation of this paper is limited. In this paper, I think the main improvement comes from taking the disparity range below 0 into consideration, eliminating the negative impact on the scheme based on distributed supervision in the disparity range below 0. But with a fixed extended disparity range, i.e., 16, I think it's hard to fit the distribution of the scenarios. Do I need to set a new extended range to fit the distribution range in a new scenario? I think this is an offset that is highly relevant to the scene.
2. I think the improvement of this method over SOTA methods such as IGEV is small. Does this mean that there is no multi-peak distribution problem in iterative optimization schemes similar to IGEV? I suggest that the author analyze the distribution of disparities produced by IGEV compared to other baselines to determine why the effect is not significantly improved on IGEV. And I have another concern. Currently, SOTA schemes are basically iterative frameworks similar to IGEV. Is it difficult for Sampling-Gaussian to significantly improve such frameworks?
2. I think the improvement of this method over SOTA methods such as IGEV is small. Does this mean that there is no multi-peak distribution problem in iterative optimization schemes similar to IGEV? I suggest that the author analyze the distribution of disparities produced by IGEV compared to other baselines to determine why the effect is not significantly improved on IGEV. And I have another concern. Currently, SOTA schemes are basically iterative frameworks similar to IGEV. Is it difficult for Sampling-Gaussian to significantly improve such frameworks?