Kati8/ICLR_2023_2025 huggingface dataset:
authors: Chuanhao Sun
authors: Thanos Triantafyllou
authors: Anthos Makris
authors: Maja Drmač
authors: Kai Xu
authors: Luo Mai
authors: Mahesh K. Marina
reviews: 
Comments: This paper proposes a Post Hoc Epistemic UQ scheme, referred as PH-DROPOUT, to make uncertainty estimation directly on pre-trained Multi view Scene Reconstruction models (NeRF and Gaussian Splatting in particular). The algorithm estimates the variance of a well-trained model by introducing binary dropout mask into model parameters and greedily select drop out ratio to perturb the output within tolerance, and validate over test views. Authors explore the usage of the measurement from their PH-DROPOUT algorithm by conducting thorough analysis over different scenarios (active learning, correlation analysis, uncertainty driven model ensembles).
Strengths: Overall it is a good discovery and reflection on how to effectively and efficiently justify and analyze the performance of NeRF or GS model quantitatively. Authors justify their measurements is sound by extensive experiments and they indicate that most NeRF or GS models tend to have more redundant parameters as training views increase. This is a bold yet very inspiring claim. It implicitly conveys an intuitive thought: the information attained from more input views shall reduce the dimension of the model itself.  

Mostly the paper context is well written and well organized, and the paper itself answers most of my concerns while I was reading it.
Weaknesses: The math claim in this paper can be improved. There are too many "colloquial" proof rather than a mathematical modeling of phenomena. These claims seems to be redundant with respect to the integrity of the paper. For instance, to represent the significant redundancy in model parameters, authors state the following in line 190:
 
$$ \exists 0\ll r<1\rightarrow \forall x\in \mathcal{D}_{train}, \lvert F(x;\theta)- F(x;D(\theta,r))\rvert<\epsilon. $$

Firstly, greater than $\ll$ is not a rigorous notation. Secondly, $\mathcal{D}_{train}$ is not defined. Is it a point set or a multi-view image set? Lastly, the proof of line 190, i.e. line 192-210, is all text and there is no anchor point to refer to appendix for a comprehensive mathematical proof, and I failed to find the corresponding complete proof of theorem 3.1 in appendix. One cannot call statement in line 192-210 a proof of theorem. My suggestion is to make it as a conjecture or rewrite this theorem. Same question for lemma 4.1, thm 4.2, thm 4.3 and thm 4.4.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 3
reviews: 
Comments: The paper introduces PH-DROPOUT, an efficient method for epistemic uncertainty quantification in view synthesis, applicable to pre-trained NeRF and GS models. It leverages model redundancy to estimate uncertainty without retraining, showing strong performance across datasets. Despite its efficiency, it faces challenges with hash encoding and sparse scenarios.
Strengths: * The introduction of PH-DROPOUT as a post hoc epistemic uncertainty quantification (UQ) method is novel and addresses a critical gap in view synthesis research.
* The authors present a strong theoretical foundation, including proofs (e.g., Theorem 3.1) that justify the redundancy in NeRF and GS models, enabling effective dropout-based UQ.
* PH-DROPOUT achieves real-time performance, outperforming prior methods in computational efficiency.
* The method shows strong correlations between UQ metrics and prediction errors (e.g., RMSE), validating its reliability.
Weaknesses: * The method struggles with hash encoding-based NeRF models, which restricts its applicability to sparse or complex scenes where hash collisions are common.
* The results on 2DGS show limitations in few-view scenarios. 
* The method is specifically designed for view synthesis tasks and may not generalize to other domains.
Recommendation: 6
Reviewer confidence: 2
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This paper proposes a fast post-hoc uncertainty quantification method for both NeRF and Gaussian Splatting Models. The method is based on test-time drop out of neurons/Gaussians in the model and estimating per-pixel uncertainty based on the error caused in the test views. The proposed method is orders of magnitude faster than other baselines and has competitive performance to them.
Strengths: - Explores how drop-out as a main UQ approach in ML, fits into the radiance field framework.
- It is applicable to both NeRF-based and 3DGS-based models.
- The proposed method is very fast, and is done post-hoc which makes it a useful method for downstream applications.
Weaknesses: Main:
- The rendering function in test time (the pre-trained radiance field) is not perfect. I do not see how comparing the rederings from the drop-out version of the model to the renderings of the model can specify uncertainty. Moreover, how are the test-views selected? Is there an assumption on the distribution of the cameras? If you select your camera far enough from the training ddistribution, the dropout rate would drop to zero , as the error from that view would be high anyway?
- The  volume rendering function itself (through its integration) hides the ambiguity and uncertainty in depth, so using the rendered image error as a source for identifying uncertainty can be less robust than a spatial method, unless queried densly from many test views.
- The authors claim this method is not architecture-dependant, a discussion about how drop-out in MLP-based NeRFs vs voxel-based or K-Plane ones results in the same metric would be useful. My confusion comes from the fact that some of these representations have more geometric meaning (like voxel or K-Planes or 3DGS) and in these cases dropping out a cell would directly affect a point in space while dropping out a node/layer from MLP might affect different places simultaneously.
- Qualitative results on uncertainty primarily reflect color uncertainty, as seen in flat regions like the train body in figure 4, where low-opacity, cloudy Gaussians exhibit low uncertainty. However, the paper should clarify whether the reported uncertainty correlates more with color error, depth error, or both, and include qualitative evidence to support this claim. This helps readers understand what use cases this uncertainty is useful for.
Minor:
- What are the PSNRs reported in Table 1? Are they PSNR after noise removal the same way explored in the baseline, if so the coverage % should also be reported alongside this metric.
- A few results on NGP-style NeRFs which are the main NeRF models used, and most results on MLP NeRFs.
- The proof sketches in the main paper can be more detailed.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 3