reviews: 
Comments: The authors propose using post-hoc dropout as a tool for epistemic uncertainty quantification in novel view synthesis frameworks like NeRF and Gaussian Splatting. Starting from a model with trained parameters, they uniformly increase the dropout rate of every parameter as long as the training loss is unharmed. Sampling from the distribution defined by this dropout rate can be used to compute spatially-varying variances in each novel view, which is used as a proxy for uncertainty, which is validated experimentally.
Strengths: I congratulate the authors for their submission. The algorithm is simple, elegant and efficient. Previous attempts at using Dropout in novel view synthesis have failed: the authors have correctly identified the promise in the recent theoretically developed dropout injection methods and ported it to the Computer Vision community and the novel view synthesis problem in particular. Regardless of how exactly it compares against prior work, it surely advances the state of the art in uncertainty quantification for Vision and will inspire future work by the community (it has inspired *me* just by reading it!).
Weaknesses: The main weakness of the current manuscript is the quality of the technical exposition. Given that what is proposed is a fairly simple extension of the Ledda et al. 2023 work, this poor quality of technical exposition is not enough to worry me about the soundness of the methods, but nonetheless the authors should strive to improve it in a revision, and this (together with the evaluation concerns below) is the reason why my score is not higher (I will gladly increase it if these questions are addressed). For example:

- The pseudocode in Page 3 has several mistakes. Line 4 should be before Line 1, and Lines 1-5 should be indented. The line starting with “Ensure” is redundant.
- The wording around \sigma_max  (L164-168) is confusing. See questions below
- The theorems and proofs in section 3.2 and 4 are incredibly vague, to the extent that I would be more comfortable if they were described as “intuitions” rather than “proofs” or “sketch of proofs”. A proof sketch is an abbreviated version of a proof for which one can be confident a motivated student reader can fill out the details. This is very much not that, as evidenced by my questions below. I would propose the authors rewrite these sections, abandon the pretence of mathematical correctness, explain their intuition and refer the reader to experiments for validation.
- After reading Lemma 4.1 and its proof 10 times, I still do not understand what it means. See questions below.

I would have also appreciated some visual examples of the uncertainty predicted in novel views by this method and its competitors that go beyond the numerical evaluations. By looking at numbers on a table, it is hard to understand what makes this method more precise than others. It claims to be “modeling all epistemic sources of uncertainty”, but this could be said of, e.g., the Goli et al. work as well, which is philosophically very similar (“how much can one modify network weights without harming the loss?”). In which image regions is this method doing a better work? Is this method better in floater removal? The text claims “Bayes Rays fails to correlate depth uncertainty with high prediction error on the LF dataset”, which seems to contradict the results in Goli et al.’s work. I would appreciate a deeper dive into this. This method is theoretically interesting and fast enough that I do not think its acceptance hinges on the evaluation being flattering, but I would expect a scientific paper to be more transparent in this regard and include a more exhaustive evaluation of where its results stand in relation to previous work, as well as its flaws and the flaws of previous works.

The manuscript would also benefit from some English proofreading (e.g., “continuosity” should be “continuity”), even if this has no bearing on my recommendation.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2