reviews: 
Comments: The paper tackles dynamic surface reconstruction and scene-flow estimation from multi-view RGB video with known camera parameters. It uses a global coordinate-based MLP that outputs the SDF in canonical space (t=0), which then gets converted to density via VolSDF's formula, enabling NeRF-style rendering. Training uses an RGB reconstruction loss and an Eikonal regularizer for the SDF. The method departs from prior dynamic NeRFs in its deformation model: a separate MLP is trained to predict the temporal change (derivative) of the SDF at any point in space. These changes get integrated with Runge-Kutta over time to obtain the time-dependent SDF. Furthermore, the paper shows how this SDF flow can be converted into scene flow. Experiments show that the geometry is either less noisy or more detailed than prior methods on the CMU Panoptic dataset and another existing dataset.
Strengths: Expanding the dynamic NeRF field in the direction of reconstruction/geometry is worthwhile in my opinion, as it enables more applications than novel view synthesis alone would. Correspondences are also interesting for the same reason. Thus, the problem setting is a big strength of the paper.

The method's SDF flow parametrization is novel, as far as I know. It seems generic enough to be potentially valuable for problem settings very different from the setting in the submission.

The illustrations are done well and the paper in general is written very well.

The method outperforms prior methods quantitatively. Qualitatively, it is better in at least one aspect compared to each prior method, while on par in the other aspects, and hence overall better.
Weaknesses: *** Method:

[Tangential Motion] The paper should more clearly state that tangential motion causes issues when converting a time-dependent SDF into scene flow.

[Topology Change] The results don't show any good instances of topology change. I do not understand this claim. The best example I could find is at 0:45. Are there any other good instances? I'm also lost as to which aspect of the method helps with topology change. Please mark that more clearly.

*** Experiments:

[Scene Flow Evaluation] The paper doesn't show any appearance/novel view results. That's okay since it focuses on the geometry, and appearance is only used as an auxiliary to deal with the RGB input. However, I would still be curious to see video results where the appearance is taken from t=0 and propagated via the SDF flow. That would allow to visualize longer-term correspondence drift and whether tangential motion causes issues in practice. Currently, the SDF flow (and thus the derived scene flow) only cares about short-term "geometrical" correspondences. - This is relevant because the paper doesn't just claim that the SDF flow is a helpful parametrization but rather that it enables scene flow, i.e. correspondences. Currently, the qualitative and quantitative scene flow evaluation is only short-term, even though the method requires long-term correspondences (equation 7). The quantitative scene flow evaluation is also only a single sequence. 

[Integral Evaluation] How does the temporal integration scale with scene length? Presumably linearly? Is that why only 24 frames are used? How many function evaluations does the Runge-Kutta solver need for the last (24th) frame? 

[Weak Qualitative Geometry] Tensor4D gives detailed, noisy results and NDR is less noisy but lacks detail. The submission's results have the level of detail of Tensor4D and the low noise level of NDR. Still, none of these results are overwhelming or that impressive. (Doesn't need to be addressed in the rebuttal.)

*** Paper:

[Related Work] The related work section isn't that thorough. For example, the dynamic NeRF papers Fang et al. Fast dynamic radiance fields with time-aware neural voxels and Li et al. Neural 3D Video Synthesis are missing. Li et al., like NSFF, can also handle topology changes. Furthermore, shape-from-template methods aren't mentioned. Scene flow methods (e.g. Song et al. PREF: Predictability Regularized Neural Motion Fields) aren't discussed. Also, since NDR is compared to, mentioning some of the papers in the RGB-D line of work would be good, e.g. DynamicFusion, VolumeDeform, KillingFusion, OcclusionFusion. And TARS (Duggal et al. Topologically-aware deformation fields for single-view 3d reconstruction) seems relevant since it isn't restricted to blend skinning (a typo ("blender") in the submission). 

[Limitations] Please discuss limitations.

*** Minor:

[Input Assumptions] Since the mathematical derivation matters here, please state what assumptions go into the paragraph between equation 5 and 6, which talks about the continuity and differentiability of a time-dependent SDF. In theory, there is nothing preventing an object from appearing out of nowhere, an SDF does not inherently have any restrictions on its temporal evolution (while it does have restrictions in space, namely the Eikonal equation). In real-world cases, geometry noise (say, due to the sensor or an imperfect reconstruction) pops randomly into existence and vanishes randomly over time, which leads to discontinuities w.r.t. the time parameter. Unless point x is meant to be a Lagrangian particle rather than an Eulerian grid coordinate? Figure 2 looks Eulerian though. --- Please state the assumptions that go into that paragraph.

[Wrong Argument] The argument at the end of Sec. 3.1 is that most dynamic NeRF methods have issues with topology changes. That's not the case for most dynamic NeRF methods that handle general objects since most condition the canonical model in some manner on time (e.g. HyperNeRF). An extreme case of that is Neural Scene Flow Fields. The argument only holds for D-NeRF-style methods like Nerfies or NR-NeRF. For the others, the better argument would be to say that they use density to parametrize geometry rather than SDF and hence the geometry tends to be very noisy and lack a clearly defined surface.

[Labelling] Figure 2 would benefit from labelling the two ellipses with their respective timesteps (t=0 and t=6?).
Recommendation: 8: accept, good paper
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 2 fair
Clarity: 3 good