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 is a good paper with new idea and solid experiments.
strengths_and_weaknesses: Strengths: 1. The idea is novel. 2. The experiments are sufficient. 3. The results are good. 
Weaknesses: Refer to the Questions.

questions: 1.Line 133, the authors design a MLP to predict the offset vectors. Is there any constrain on the MLP? Could you please add a figure (in the supp.) to show the shifted points or superpoints? Could you please add one experiment that without the offset vectors?
2.Line 172, for nodes u and v, if they are from different instances, the triangle areas are also  minimized, just as they are from the same instance. Please explain it in detail.
limitations: Please refer to the Questions.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This work tackles the task of 3D semantic instance segmentation by exploiting the local geometry. For that purpose, a graph is defined over geometric super-points. Then a series of graph cut networks, edge scoring functions and attention mechanisms is used to extract, classify and score the final instance mask. Experiments on S3DIS A5/6fCV and ScanNet test outperform prior published methods. Ablation studies on ScanNet validation show the effect of the individual model components.
strengths_and_weaknesses: **Strength**

The proposed approach is original in terms of the model architecture which is inspired by traditional graph cut approaches and achieves state-of-the-art 3D instance segmentation scores on two popular indoor scenes datasets (ScanNet, S3DIS).

**Weaknesses**

The model itself consists of numerous components that are not always very clearly explained and/or motivated. For example, l.122 mentions sparse convolutions, but it is not very clear if they are applied on points or on superpoints. Then additional edge-conditioned convolutions are applied. What is the motivation for two types of convolutions, wouldn’t one type of convolutions be enough? This could be further motivated and also evaluated to show that it actually improves performance. Overall, the model consists of numerous components which increases complexity and raises the question if a similar performance could be achieved with a simpler model, allowing the community to draw more general conclusions. At this stage, it is an interesting model that performs very well but I’m unsure about the significance to the community since it is unclear which conclusions to draw that can push the field forward.

The approach also relies on a large number of hyper-parameters (l.184, l.192, l.201, l.292 …), requiring manual tuning. The paper contains parameter studies only for a subset of them e.g. k in k-NN. From the paper it is unclear if the same hyperparameters are used for both datasets and how sensitive the approach is to the parameters.
questions: **Question**

The method introduces some new triangle losses, but it does not help? Table 3 in the supplementary seems to suggest that the L_area hurts the performance. However, Table 5 in the main paper suggests the opposite conclusion. What is the correct answer?

**Minor suggestions**

- l.133/149 - repeat meaning of F.
- Fig.1 - add the losses to the model figure (now it’s a bit unclear where they are applied), similarly add the used notation into the Figure - 
- l.184 Parameter study for delta and beta?
- l.191 Where do the semantic classes come from?
- l.201 Parameter study?
- l.230 How is the scalar score obtained from the cross entropy loss / per-?
- l.235 Where is the semantic loss applied?
- l.278 Highlighting that the model produces clear boundaries is not that insightful - this is clear from the fact that the model is based on an over-segmentation which always produces clear boundaries.
- Training details: is the model trained in the train and val split for the test set submission?

limitations: Limitations and negative societal impact are adequately discussed in the rebuttal. In particular, the limitations include sensitivity to the long-tail problem, i.e, refrigerators which appear sparsely in the training data and expose a large intra-class variety. The papers also suggest potential solutions such as mining context information or data augmentation.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes a learnable superpoint-based graph cut method to explicitly learn the geometric structures for 3D point cloud instance problem.  Experiments on benchmark datasets show that the proposed method outperforms current state-of-the-art methods.
strengths_and_weaknesses: Strengths: 
The proposed approach which dynamically prunes the unrelated edges for better instance segmentation is relatively new. The paper presents a detailed description on the main building blocks (i) edge score prediction network and ii) superpoint graph cut network. And the ablation analysis validates the efficacy of the proposed network structure and geometry-aware edge loss.

Weaknesses:
Strictly speaking, the proposed approach is not completely learnable, since the initial superpoint graph oversegmented from point cloud is determined empirically. What is the sensitivity analysis on the different configurations on the initial superpoint graph?
questions: Using attention to capture the similarities between points or nodes in graph is not a completely novel approach, there have already been some existing works [1] in 3D point cloud proposed before. What are the main differences of the author’s attention-based approach? It is suggested to append this mention in the Related Work section.

[1] Wang, Lei, et al. "Graph attention convolution for point cloud semantic segmentation." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.

limitations: What I’m more concerned about is the runtime efficiency on the proposed learnable graph cut method. For deploying the proposed framework in the autonomous driving application as mentioned in the Introduction section, a short response time is desired. From the supplemental, however, the runtime efficiency comparison only is performed on indoor datasets (S3DIS, ScanNet2). Outdoor dataset specific to autonomous driving such as SemanticKITTI should be the more suitable one for evaluation.
review_title: null

Meta-review: metareview: The paper follows the recent trend of segmenting point clouds into instances using superpoints. Technically, the paper proposes to construct superpoint graph, and learns to perform graph cut for the segmentation. Three reviewers generally appreciate the idea and empirical results, although some architectural components and hyperparameters are less evaluated. 

After the rebuttal phase, three reviewers hold their original decision on accepting the paper. AC agrees.  Congratulations!

confidence: Certain
Decision: Accept