reviews: 
Comments: The paper proposes a simple yet effective and extremely fast ad-hoc method for epistemic uncertainty estimation that operates directly on pre-trained NeRF and GS models in the task of novel view synthesis. At its core, the method proposes to use dropout at test time with the maximal drop rate that maintains an epsilon fit to the training views. The method is validated through proxy metrics like Spearman’s correlation of the uncertainty estimates with the RMSE from the GT, and the trend as a function of the number of training views. In addition, the authors demonstrate improved ensembling based on optimal ensemble member selection using their uncertainty estimates.
Strengths: * The authors considered multiple quantitative proxy metrics to benchmark their method, showcasing extensive evaluations.  
* The method is very simple to implement and adopt, yet it is extremely effective and efficient in terms of runtime.  
* Overall the paper is structured well and is easy to follow.  
* Related work is acknowledged, and the contribution of this paper is put in proper context.
Weaknesses: * Perhaps the major weakness of this paper is the relatively incremental contribution compared to Ledda et al 2023, which practically proposed the same technique up to the differences mentioned by the authors in L169-175. While I sincerely appreciate the honest citing of this related work by the authors, I find the contribution of PH-DROPOUT to be relatively small.    
* The English writing of the paper can be significantly improved in certain spots (see small list below), although for the most part it is not hard to understand the authors intention based on the context.  
* The proofs in the paper need to be more rigorous. For example, L192-207 are very hard to follow. Similarly, L235-243 seem to have mathematical inaccuracies such as calculating the KL divergence between two parameter instances instead of between two distributions. This overall proof sketch is not very clear and requires significant editing. The same applies to Theorem 4.2 and its proof sketch. More rigorous mathematical definitions and notations are needed. In the full proof from the appendix L777-782 seem to have a mistake in the variance formula. Where did $N\_F$ come from?

My rating is mainly due to the relatively limited contribution compared to Ledda et al. and the lack of mathematical rigor in the proofs. Nonetheless, I’m willing to increase my score if the authors' rebuttal can alleviate my concerns with respect to these two issues, as I still think this work does have the potential for practical value in quantifying the uncertainty of novel view synthesis. 

A few caught (minor) typos/english corrections:

* L103 \- hence hard \-\> hence **it is** hard  
* L104 \- find \-\> found  
* L128 \- in **ad** network \-\> in **a** network  
* L129 \- inject \-\> inject**ing**  
* L130 \- **non-**trivial \-\> **not** trivial  
* L149 \- step 3 in the algorithm is better split into 2 lines/formatted differently  
* L162 \- need \-\> need**ed**  
* L184 \- a common features \-\> common features/a common feature  
* L185 \- method \-\> method**s**  
* L198 \- signal \-\> **the** signal  
* L201 \- what does a “function with nearly discrete pattern” mean?  
* L208 of \-\> of **a**  
* L225 \- spa**c**ial \-\> spa**t**ial  
* L229 \- same structur**al** \-\> same **structure**  
* L229 \- number of parameter \-\> number of parameter**s**  
* L229 \- have similar distribution of parameter \-\> have **a** similar distribution of parameter**s**  
* L235 \- continuou**sity** \-\> continuity  
* ….
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 3