Kati8/ICLR_2023_2025 huggingface dataset:
authors: Wei Mao
authors: Richard Hartley
authors: Mathieu Salzmann
authors: miaomiao Liu
reviews: 
Comments: This paper proposes a novel representation, namely SDF Flow, for solving multi-view dynamic scene reconstruction by representing the dynamic scene as a 4-D SDF field and modelling its derivative w.r.t. time instead of the SDF value. The proposed representation has several nice properties and can be used to compute the scene flow analytically. Experimental results on public datasets have shown competitive reconstruction quality compared to state-of-the-art, and better scene flow estimation.
Strengths: 1. The proposed representation is novel. I’ve never seen similar representations before. The idea is clear and elegant but effective. It has the potential to open up a new paradigm for dynamic scene reconstruction and provide great insight for the community.

2. The authors also revealed the relationship between SDF flow and scene flow through mathematical derivation, and have shown that the scene motion can be computed analytically by solving linear equations.

3. Quantitative and qualitative experiments have shown the proposed method could achieve promising reconstruction quality. The scene flow estimation result also looks promising.
Weaknesses: 1. In Sec 3.3, the authors made two assumptions when deriving the computation of scene motion from SDF flow, which is justified by a 2D toy example. However, it would be great to have a more in-depth theoretical analysis and experiments to understand its convergence behaviour. Especially for assumption 2, the difference between assumed $\Delta_S$ and real value looks quite big.

2. The optimisation takes too long, which limits its practical usage.
Recommendation: 8: accept, good paper
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 3 good
reviews: 
Comments: This paper proposes to predict the first-order derivative of a signed distance function (SDF) at a point, termed as SDFflow. By using SDFlow, the dynamic recontruction to recover a SDF at time t can be treated as space-time integration of the SDFflow from time t_0 to t and its starting SDF at time t_0. The authors further demonstrate how locally rigid scene-flow can be recovered least-square optimization given the locally rigid assumption for small motions. The results demonstrate the method is outperforming previous baselines using alternative shape and flow representation.
Strengths: * The approach is technically sound. It is kinda intuitive that this representation should work. 
* From the quantitative comparison, it is clear the method is outperforming previous methods by a relatively large margin. 
* The author also provides connection of SDF flow to scene flow with a math derivation. It is helpful for readers to understand its connection if without background in optimization based motion estimation.
Weaknesses: * The flow evaluation in the paper is very weak. The method only compares to NDR on 2D project error using RAFT as pesudo ground truth. This should not be hard to achieve if using a synthetic dataset, if using the rigid moving toy example being shown in the paper. 
* The flow estimation also lacks details. Solving the least-square optimization requires sample multiple points, which the author say "we select more than 6 points" and sometimes it still be ill-conditioned depending on the property of the sampled points, but I don't see any discussion related to this and how the results are generated. 
* The main contribution of this paper is the SDFlow and a claim that it is beating alternative representation (SDF with warping field for example). This can be much clearer if the authors can provide more ablations studies on the contrast of the two representations. Two small experiments they author can simply do is to 1) have the network predict the integral of flow in eq. (7), or 2) predict the time dependent SDF s(x, t), all with fixed hyper-parameters. The contrasts in this ablation can reflect the performance difference in predicting SDF flow. Though the authors provide comparisons to alternative papers that the other papers compound too many other terms, and I don't think I can get the insights of why SDF flow works better here.
Recommendation: 8: accept, good paper
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 2 fair