Kati8/ICLR_2023_2025 huggingface dataset:
authors: Yanqi Bao
authors: Tianyu Ding
authors: Jing Huo
authors: Wenbin Li
authors: Yuxin Li
authors: Yang Gao
reviews: 
Comments: This paper presents a method called InsertNeRF, which aims to instill generalizability into Neural Radiance Fields (NeRF) without extensive modifications to the vanilla NeRF framework. The method utilizes multiple plug-and-play HyperNet modules to dynamically tailor NeRF's weights to specific reference scenes, allowing for more accurate and efficient representations of complex appearances and geometries. The main contributions of InsertNeRF are: (1) introducing a novel paradigm that inserts HyperNet modules into NeRF-like systems to achieve generalizability, (2) designing two types of HyperNet module structures tailored to different NeRF attributes, and (3) demonstrating state-of-the-art performance and potential in various NeRF-like systems. The paper also discusses related works on generalizable NeRF and hyper-networks, and provides an overview of the method's background and implementation details.
Strengths: -The idea of using HyperNet to solve the important problem of generalizable NeRF is a very good and promising pipeline. Thus, the novelty of the proposed method is very strong. 
-The proposed InsertNeRF can achieve the generalizability without extensive modifications to the vanilla NeRF framework. 
-HyperNet modules can dynamically tailor NeRF's weights to specific reference scenes, allowing for more accurate and efficient representations. 
- The proposed InsertNeRF achieves superior generalization performance and can be integrated with other NeRF-like systems. It also demonstrates state-of-the-art performance and potential in various NeRF-like systems, even in sparse input settings.
- The writing and the presentation of the paper is good.
Weaknesses: -All the contributions, points, and the details of methods and experiments have been clearly presented.
Recommendation: 10: strong accept, should be highlighted at the conference
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: 4 excellent
Clarity: 3 good
reviews: 
Comments: In the paper authors use multiple plug-and-play hypernetworks modules in NeRF based models to obtain generalization properties. The proposed method can be integrate with many different NeRF-based models.
Strengths: 1. The papers obtain good experimental results.
2. The framework can be used for many different NeRF architectures
Weaknesses: 1. The paper is hard to read. It is unclear what is input to new meeting components. 
2. It is hard to understand the general idea of the model, and Fig. 2 is completely unclear.
3. Genera formulas (3) and (4) are unceler.
4. In the experimental section we do not have experiments with the ShapeNet-based dataset (see pixelNeRF).
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 2 fair
Clarity: 1 poor
reviews: 
Comments: The paper targets the task of NeRF generalization, and proposes the HyperNet module before each MLP layer of vanilla NeRF, inspired by the concept of hypernetwork. The proposed method achieves comparable or even better performance compared to previous works.
Strengths: Such a plug-and-play hypernetwork-based method can bring an extra performance boost for NeRF generalization, which is proved to be useful.

Comparison to previous works and several ablation studies were performed to verify the effectiveness of the aggregation strategy and each component proposed in the HyperNet module.
Weaknesses: The paper may need more explanations or descriptions about the insight of the method, to demonstrate why such a strategy or technical designs could help NeRF generalization—the analysis of ``why'' is also important. 

The experiments are very simple and short, in Figure 3, the methods are close in visualizations. As a prior and baseline work of generalizable work, IBRNet shows close performance to the proposed InsertNeRF.
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 2 fair