Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: This paper presents a method for point cloud segmentation specifically focused on biological applications. The proposed method defines a representation of the input point cloud using the pairwise distances between points. This representation is fed into deep neural network with attention layers in order to obtain the predicted instance segmentation.
strength_and_weaknesses: I have several concerns regarding this paper. First of all, it seems to me that this paper is focused on a specific applications (i.e., segmentation of biological structures in electron microscopy), which might be of limited interest for the ICLR audience. In addition, I think that the theoretical contribution of the proposed method is limited, it doesn't seem to me that the proposed network is very innovative, since it is a quite standard network with multi head attention. I have also some concerns on the experiments validation of the proposed method: in the performance comparison the authors do not consider the most well-known state-of-the-art methods for point cloud segmentation (in particular in the experiment on ScanNetV2 in Table 2). For this reason, it is not possible to assess the performance of the proposed method. 

Minor comment:
the authors do not used the correct template for ICLR.
clarity,_quality,_novelty_and_reproducibility: As discussed in the previous section, I think that the quality of this work is low with a reduced impact in the ICLR research community and a limited novelty.
summary_of_the_review: This paper presents an application of deep learning to a very specific application field (i.e., segmentation of biological structures in electron microscopy). I believe that this application is of limited interest for the ICLR community and the technical novelty of the proposed method is very limited. For these reasons, I don't think that ICLR is the correct venue for this work.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: This paper presents a graph-based method to solve point-cloud instance segmentation. The main idea is to train an attention-based deep network that predicts the probability of each pair of points belonging to the same instance. The first step of the proposed method builds a similarity matrix on the point cloud. Each entry of the similarity matrix stores the pairwise edge weight between the corresponding pair of points. The edge weights are derived from Euclidean distances between points plugged into an exponential-decay formula. The second step is built on multi-head attention modules and triangular multiplicative updates to refine the edge weights such that the probabilities of points belonging to the same instances can be better estimated. The final step is simply to obtain connected components by thresholding on the edge probabilities. The proposed method is evaluated on EM data and ScanNet v2.
strength_and_weaknesses: 
**Weaknesses**

The main weakness of this work is that its problem formulation and experiment setting seem to be different from those adopted in other instance segmentation approaches.  The proposed method actually aims to solve the point-cloud clustering problem. 

It is unclear how the class label of each instance is obtained. 
In the paragraph of **ground truth generation*, it is described that "*the ScanNetV2 dataset consists of semantically labeled point clouds obtained from LIDAR scanned scenes. In this case, we construct the graph by labeling as $1$ each $P_{i,j}$, if $i$ is in the same instance as $j$ and $0$ if the distance from $j$ to $i$ is less than the average kNN distance between all points. In both cases, the instances are connected subgraphs of the full point cloud graph.*"

So, based on the description above and the procedures mentioned in Section 3, the proposed method only predicts the connected components of the graph as object instances. For the instance segmentation task on ScanNet v2, each object instance should also be assigned the correct semantic label. It is not clear how the results reported in Table 3 are obtained.

clarity,_quality,_novelty_and_reproducibility: **Clarity**

The writing is clear in general. Some details of algorithm designs and experiment settings need further clarification. 

**Quality**

The minus sign in the formula of edge weights is missing. it should be $e_{i,j}=\exp(-0.5 \cdot d^2_{i,j} /s^2)$.

The claim of "*DIST can be applied to other point cloud instance segmentation problems and extends easily to arbitrary dimension point clouds.*" is not well justified, given that the proposed method is not yet able to achieve state-of-the-art performance on ScanNet v2. It is not clear whether the proposed method can indeed provide significant advantages to the problems of point-cloud instance segmentation or even object tracking over time in 3D imaging.

**Novelty**

Applying graph-based representations to the modeling of point cloud data is not new. Using multi-head attention and triangular multiplicative updates to refine the graph Laplacian instead of using transductive propagation might be new. 

**Reproducibility**

Pseudocode is included in the appendix.

summary_of_the_review: The proposed method is straightforward. However, the main issue of this work is that its problem formulation and experiment setting do not align with the previous methods.
Although the proposed method is aimed to solve the task of point-cloud instance segmentation, it seems that it only solves point-cloud clustering. It is unclear how the class label of each instance is predicted. The authors need to clarify what is the real problem that the proposed method can address well and choose a more suitable benchmark for evaluation.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper presents a method for segmenting point clouds by predicting an adjacency graph between the points using a transformer that refines the edge weights of an initial graph. The final segmentation is obtained by graph cuts on the predicted adjacency graph. The method shows good promise in obtaining accurate segmentations for cells in microscopy images.
strength_and_weaknesses: Strengths:
- The method is quite general and can be used for segmenting both elongated and blob-like structures
- The result is rotation invariant because it is based on distances
- The results are very good, especially for the microscopy images

Weaknesses:
- Related work is missing a discussion of how the proposed method is different from Jumper et al 2021, from which they take some modules.
- The method scales as n^3 with the number n of points. The authors use cropping to reduce the number of nodes processed at one time and then patch the partial results together. 
clarity,_quality,_novelty_and_reproducibility: The paper is quite clear and easy to understand. 
However, there are a number of typos, e.g. "experience microscopist" in page 6 should be "experienced microscopist". Also, the numbers have a weird notation, e.g. "20'000" instead of "20,000".
Some might argue that the method has minimal novelty because it just combines together different techniques from other papers (MHA module from Vasvani et al 2017, triangular update from Jumper et al 2021). However, I see those as building blocks and the proposed method builds on them to obtain a novel solution to a different problem.
summary_of_the_review: The paper shows good promise in providing a novel solution to the problem of instance segmentation from point clouds. The paper's strengths of being generic and obtaining good results outweigh its weaknesses of being computationally expensive and missing discussion with some existing work.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The paper treats instance segmentation as a graph learning problem, via casting a point cloud as a graph, followed by a feature extraction pipeline of applying the multi-head attention (MHA) module [1] and triangular multiplicative updates [2]. The final instance segmentation predictions are obtained by finding the connected components in the graph using a graph cut. Experimental results show that DIST archives competitive results in membrane and microtubule segmentation, and instance segmentation in natural 3D scene scans.
strength_and_weaknesses: The authors tackle an important research problem of point cloud instance segmentation and highlight the importance of accurate instance segmentation to the cryo-electron microscopy tasks, which looks promising for future exploration.
The authors developed some pre-processing techniques to handle the issues of uneven point spacing  (via point cloud re-scaling) and large memory overhead (via cropping and stitching), all of which are useful in practical applications.


There exist some terminologies without further explanation.  For example, the authors referred to their method as a Dimensionless Instance Segmentation Transformer (DIST) but never gave an explanation of the ‘’Dimensionless“ part of the DIST. The authors mentioned SO(n) invariant property for DIST but never formally demonstrated it.

The presentation quality of the paper is not good. For example, Tables 1, 2, 3, and 4 are presented but never get referred to in the main paper. The reviewer can not find Tables 4.1 and 4.2 and Figure 4.1 mentioned multiple times by the authors. All these issues cause significant difficulty to understand the paper. The reviewer tends to believe that this paper is prepared in a rush with poor presentation quality.

clarity,_quality,_novelty_and_reproducibility: The reviewer has concerns about the novelty and tech contributions. The developed approach falls short in them given the fact that they merely adopted the existing known techniques including the multi-head attention (MHA) module [1] and triangular multiplicative updates [2] without adding a further major innovation. Additionally, the rational explanation for choosing these components remains untouched, which is likely to confuse the reader and is less useful for understanding the paper. The take-home message seems to just apply the advanced designs in attention models with an efficient way to update edge features and the paper is limited in novelty and tech contributions.
summary_of_the_review: Overall, this paper has a major issue with novelty, tech contribution, and paper presentation. The reviewer tends to give rejection to the current paper due to this reason.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The paper proposes a method for point cloud segmentation on biologic data leveraging pairwise distances between points and multi-head attention. The performance in the main experiments seems good, though some reviewers feel the comparison with existing work is not thorough. The major weakness of the paper includes marginal novelty, missing comparison and discussion to latest literatures, and some issues in writing. 
justification_for_why_not_higher_score: There are major limitations of the work in whether the work is of broad interest, lacking comparison with latest point cloud segmentation networks, and exposition. Given its current status, the meta-reviewer does not recommend to accept the submission.
Decision: Reject