Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This work explores the problem of few-shot learning in 3D object detection. It appears to be the first work in the field, and contributes 2 dataset-settings, based on SUNRGB and ScanNet, as well as 4 benchmarks, mostly based on VoteNet. The authors contribute their own method, which extends VoteNet to incorporate prototypes in each of the feature representation “stages”. The prototypes are moving averages of the closest features. The method is simple, yet effective, showing strong performance gains compared to the other VoteNet baselines.
strengths_and_weaknesses: This work, to the best of my knowledge, is one of the first works to tackle the problem of few-shot learning in a 3D object detection setting. In addition to their own method, the authors also contribute a set of benchmarks and a proposed dataset setting to evaluate future few-shot learning methods. This is a good contribution to the community as a whole. The method proposed by the author appears to achieve strong performance compared to proposed benchmarks. The method appears to be informed, and empirical evidence backs up each module’s necessity. By initializing the prototypes used for the features (“geometric”) and for the classes with the feature-space centroids and updating with a moving average, it seems to prevent the few-shot learner from overfitting as much (table 1,2). Experimentally, the ablation section is detailed and I do not have any questions after reading it. Nice to see the prototypes didn’t collapse (fig 3).

The conclusion in section 3.2, L162 that the prototypes are learning basic 3D geometric shapes is a bit misleading, since distribution of features in any pre-trained detection model should be similar for similar objects as well (fig 3). Considering the prototypes are updated in the feature space, there is nothing to indicate that there is 3D geometric interpretation; one way to visualize if it truly is learning basic geometric shapes is to optimize a shape input to maximize one prototype. This would give a better “geometric” interpretation of what that prototype is representing. However, this is not necessarily a weakness, since feature-space prototypes are just as useful, but I would recommend either changing the claim, or backing up the claim with visualized representations of what each prototype corresponds to in 3D input space. 
One weakness is the benchmarks proposed are all built on top of VoteNet. A benchmark that would be good to include is KNN assignment to the closest support class. This would be able to leverage other detectors.

Miscellaneous:
L121: “Taking a point cloud scene P_i as input… localize and categorize…” → “VoteNet takes a point cloud scene P_i as input and localizes and categorizes…”
Fig 4 caption: “Tsne” → “t-SNE”
L225: “Till now” → “Until now”
questions: Were other methods of updating the prototypes tried? Does setting the prototype at the end (no updates) perform well? (not necessarily a weakness, just a curious possible experiment)
limitations: The authors did not discuss any limitations.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: The paper presents a method for few-shot 3D point cloud object detection. The method extends VoteNet by incorporating two new modules, prototypical vote module (PVM) and prototypical head module (PHM). The experimental results show that the proposed prototypical VoteNet improves the performance for few-shot t 3D point cloud object detection.
strengths_and_weaknesses: Strengths:

- The paper addresses a new problem, which is potentially useful.
- The method is plausible and seems to work better than existing methods.

Weaknesses:

- The technical components are largely adapted from existing papers, so technical novelty is somewhat limited.
- The few-shot setting is not sufficiently convincing. The categories chosen for few-shot learning are not really rare categories, so it is not sufficiently meaningful. The performance is also rather low for practical use.
- The datasets are straightforward adaptations of well-known datasets, so they should be declared as a contribution. 
- The paper only compares the baseline VoteNet, not follow-up improvements which could give better performance. 


questions: - How are the new components different from existing work, such as [37]?
- How are the hyperparameters such as alpha_1, alpha_2, etc. set?

limitations: The limitations of the paper are not sufficiently discussed. Given the relatively low performance, the paper should include more discussions of limitations beyond just resolution difference.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes a new task, few-shot 3d point cloud object detection. It is a combination of two well-studied topics, few-shot learning and 3d point cloud detection. And naturally, two well-known methods from both sides, prototypical learning and VoteNet, are combined to tackle the new task.

While the combination seems straight forward, the paper further shows that it is not trivial. The implementation of PVM and PHM disentangles the feature embedding and detection. And successfully 
strengths_and_weaknesses: It is overall a good work. Strengths are already covered in summary. Here are some weaknesses.

1. As a general problem when a few-shot detection benchmark is composed, do we have novel classes in training set but annotated as background? What is is ratio?

2. Can you visualize some typical structures of geometric prototypes? For example, the neighborhood of the point with features close to each prototype?

3. The abbreviation FSOD is usually used for few-shot object detection (2D). As a novel task for 3D, the name could be more specific, such as FS3D.
questions: Questions are listed in weaknesses.
limitations: The limitations of the work has not been addressed.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: This paper proposes a few-shot framework for 3D point cloud indoor object detection. They propose prototypical votenet to recognize
and localize novel instances based on the PVM and PHM modules. PVM leverages class-agnostic geometric prototypes learned from base classes to refine local features of novel categories. PHM is designed to utilize class prototypes to enhance the global feature of each object. This paper also provides two new benchmark datasets, FS-ScanNet and FS-SUNRGBD. They conduct extensive experiments to demonstrate the effectiveness of their method, which shows a promising performance compared to several self-designed baselines on two benchmark datasets.
strengths_and_weaknesses: Strengths:

-This paper is well-written and easy to follow.

-The idea is interesting. This paper considers utilizing geometric learning and object prototypes to assist few-shot 3D object detection, which will help our community. 

-The results of their experiment are promising with a remarkable margin.

Weakness:

-The baselines of this paper are not well-designed. Few-shot object detection in 2D community is well-studied. Why didn't this paper compare some classical few-shot approaches in 2D community? I don't think it's difficult to transform some 2D techniques into the 3D domain in this task. It's difficult to judge their contribution based on these weak baselines. 

-If you statistics them, both of these datasets i.e. ScanNet V2 and SUN RGB-D are unbalanced. In ScanNetV2, the number of chairs is 4357 and 113 of bathtubs. If this paper focuses on solving the imbalanced problem of different classes by considering the prototype learning, it would contribute more to the 3D community. So I want to see the improvement of fully training based on the votenet and proposed PVM&PHM. That means the base class is the whole class (18 classes) and no novel class.
questions: All the questions are mentioned in Weakness.

-I want to see more baseline approaches widely used in 2D few-shot object detection.

-I want to see the improvement of the whole class by the proposed model. (Compared with VoteNet).

If the authors solve my concerns, I will consider raising my score.
limitations: No
review_title: null

Meta-review: metareview: The paper received mixed reviews. Two reviewers were fairly positive, on the basis of of the novelty of the problem, the quality of the results, the introduction of datasets and benchmarks, and the proposed method. Although the method combines two existing solutions to point cloud detection and few-shot, these reviewers considered that the paper shows that this combination is not trivial. They liked the implementation of the PVM and PHM modules as a means of disentangling the feature embedding and detection. The negative reviewers raised a number of concerns, including a diverging opinion that the combination of the two strategies is somewhat trivial and the paper lacks technical novelty, the fact that the datasets are mostly a combination of existing ones, that several baselines could have been borrowed from the 2D few shot literature for a more extensive evaluation, and other concerns of detail. The authors provided a very thorough rebuttal, which addressed many of these issues. In result, one of the negative reviewers mentioned that, despite the limitations, the paper is worth publishing and the other engaged in an extensive discussion with the authors, oscillating between positive and negative positions towards the paper at different points of the interaction. After discussion, there was a sense that no reviewer significantly opposed the publication of the paper. While the limitations above (somewhat limited technical and dataset novelty) hold, the novel nature of the problem, its potential interest for future work by the community, and the results achieved by the method were found to justify publication.
confidence: Certain
Decision: Accept