Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper introduces a novel method to generate triangular meshes for the the input 3D point cloud. The authors introduce multiple anchors  to divide the neighborhood space of each input 3D point. A deep neural network is then used to predict the presence and the locations of cirumcenters in the point local neighborhood space. Then the dual of the circumcenter is used to extract triangular mesh.
Contributions:-
1. This paper shows that their method is more efficient and accurate than the state of the art learning based techniques on point cloud triangulation.
2. The proposed method generalizes on real scenes with noisy data and doesn't hold restrictions on mesh topology as in contrast to some other learning based techniques. 

strength_and_weaknesses: Strengths:-
1. A novel method to generate  triangular meshes for the the input 3D point cloud. First learning based technique to generate point cloud by estimating the cirumcenters in point local neighborhood space.
2. Method seems to generalize on real scenes with noisy data. 
3. Method is much faster than other state of the art learning based techniques.
4. Theoretical foundations of the paper seem to be correct.

Weakness:-
1. The paper lacks to provide enough visual evidences of real benefits of the paper over standard point cloud triangulation techniques. In my opinion, the authors should make it very clear how is accuracy improving in comparison to other techniques. While, some metrics like chamfer dist. (and edge distance) speak for the accuracy, I believe visual aspect of highlighting the regions where accuracy or completeness is improved increases the actual impact and brings more clarity for the reader.  Authors can highlight regions where standard techniques struggle, for example, thin structures, bad triangulation (non-manifold errors). 
2. Although, it is fair to say implicit surface representation works are fundamentally different than mesh triangulation but I guess comparing results with such methods might highlight the strengths/weakness of both classes of methods. This enables complete and higher level study to understand true value of choosing learning based mesh triangulation over something like Poisson surface reconstruction.

clarity,_quality,_novelty_and_reproducibility: 1. Quality of results can be improved by providing the zoomed in regions where accuracy of the system lies in comparison to other state of the art methods. Particularly, highlighting thin surface triangulation would bring out more trust on the accuracy of the method. 
2. Being more liberal in comparing with different classes of methods reconstructing surfaces from points will highlight benefits of choosing learning based mesh-triangulation over other classes of methods.
summary_of_the_review: The paper introduces a novel method to generate triangular meshes for the the input 3D point cloud. The authors use a deep neural network  to predict the presence and the locations of circumcenters in the point local neighborhood space. Then the dual of the circumcenter is used to extract triangular mesh. The paper provides some evidence that their method is more efficient and accurate than the state of the art mesh triangulation techniques. The impact of the paper can be improved by improving the quality of visualization of the results by highlighting, the regions of improvements. Discussing the results with other class of methods such as implicit surface reconstruction ones will bring out true benefits and impact of such method.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The authors propose a method for creating a mesh from point clouds taking advantage of learning-based techniques.

The main contribution to the paper is the use of the duality relationship between the mesh triangle and the respective circumcenter, in which each triangle vertex is equally distant from the circumcenter. This is done using a deep neural network.

strength_and_weaknesses: Strength:
1) The paper is well-written and easy to follow.
2) The method is technically sound.
3) Although the method does not beat the state-of-the-art in runtime neither surface quality, authors show a significant speedup compared with learning-based baselines for good surface quality.

Weaknesses:
1) The method is still slow compared to the traditional baselines.

Issues/questions:
1) It would be good to define an "optimal triangle";
2) The authors mention that the final mesh is obtained by filling small holes in the primitive mesh. It is unclear to me why is this needed, and how are they being filled.
3) The authors use Anchor priors using spherical coordinates. The motivation for the use of spherical coordinates is not clear to me.
clarity,_quality,_novelty_and_reproducibility: The paper is easy to follow, and the writing is OK, and the method can be reproduced.


summary_of_the_review: The paper has some interesting contributions and, although the proposed meshing is still slower than the traditional baselines, results show that it is significantly faster than previous learning-based methods.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The paper proposes a method for point cloud triangulation by creating local per-point triangulations and then post-processing these local triangulations into a global point cloud triangulation. The first step is performed using a graph neural network and it is the main contribution of the paper. Specifically, the paper proposes to predict the presence and the location of the circumcenters of the triangles around each point using a graph neural network. The paper further proposes to discretize the space around each point into predefined anchor cell and predict the circumcenters inside these anchor cells.   
strength_and_weaknesses: Strengths
* The paper is clearly written and easy to follow.
* The paper proposes a novel method to predict local per-point triangulation of a point cloud with a graph neural network.

Weaknesses
* The proposed deep learning method only predicts local per-point triangulation (1-ring neighborhood). Even this steps does not provide any guarantees of the validity or quality of the local triangulation - e.g. can the triangles overlaps? Some experiments and metrics need to be added to illustrate and validate this step.
* The main weakness of the proposed method is the fact that it does not discuss in any detail how these per-point triangulations are post-processed into a valid (e.g. non-overlapping triangles) and useable (e.g. for rendering) triangulation of the whole point cloud. Since nothing in the network design constraints neighboring points to have consistent triangulations after the inference, the postprocessing step seems to be essential to fix the inconsistencies. The paper also does not provide any metrics and almost no visualizations to illustrate the validity and useability of the reconstructed meshes. In addition, it is mentioned in the paper that a C implementation of the postprocessing is provided, however it does not appear in the attached code submission. 
clarity,_quality,_novelty_and_reproducibility: The paper is mostly clearly written and easy to follow. Most of the detail necessary to reproduce the paper seems to appear in the main text or the supplementary material. The attached code includes the network implementation and validation code - however it does not include the code for training the network, or the code for post-processing the network output (local per-point triangulations) into a global triangulation.
summary_of_the_review: * The attached code does not include the code for training the model, or for post-processing the network output (local per-point triangulations) into a global triangulation.

* Not all parameter choices are explained - e.g. how is M chosen (Sec. 3.1).

* In section 3.1 and Figure 4: what constraints the network to produce valid local triangulations? E.g. that the triangles in this 1-ring neighborhood of a point are non-overlapping. Provide some quantitative experiments.

* I suggest to move Eq. (7) and the preceding sentence starting with "The proposed network detects each circumcenter" to section 3.2, to explain how anchors are used to predict circumcenters. Also, use “hat” above "g"s in Eq. (7) to specify that these are offsets from predicted circumcenters, not from the ground truth ones.

* At the end of Section 3.5, the paper states "Since the primitive mesh is produced regardless of topological constraint, we apply post-processing to make it edge-manifold and fill the small holes. For convenience, we provide C implementations with Python interface for the post-processing." This post-processing seems like a very important part of the proposed method. Since nothing constraints neighboring points to have consistent triangulations after the inference, the postprocessing step is essential to fix the inconsistencies and to produce consistent and useable point cloud triangulation. For such an important step, the two line explanation which does not contain any detail about the method is not sufficient. Also, as stated above, the code linked to the submission does not include the postprocessing code.

* Evaluation criteria: Elaborate on why CD1, CD2, F1 are the right metrics here. E.g. what exactly does Chamfer distance measure here and how it is related to the quality of the triangulation. Add an evaluation metric measuring the quality of the constructed meshes in terms of how well they approximate 2D surfaces.

* Figure 5:
    * (a) Add visualization of a small parts of the mesh showing the quality of the triangulation. Render the resulting mesh to show the triangulation quality visually.
    * (d) The triangulation seems to contain holes for non-uniformly sampled point cloud, contradicting the statement in the caption that the method is robust to variations in the point cloud density.
    * The caption is very lengthy and can be improved by moving come explanations to the main text.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper tackles the problem of point cloud triangulation. The authors introduce a deep neural network to detect circumcenters from point patches, and then they extract triangles dual to the detected circumcenters to form a primiteive mesh. The proposed method can reconstruct meshes within a couple of seconds, and poses no restriction on the surface topology. Evaluation in terms of quality, efficiency, and robustness has been carried out on 4 prominent datasets.
strength_and_weaknesses: Strength
+ The formulation of the problem as circumcenter detection sounds novel in deep learning based point cloud triangulation.
+ The formulation of the problem as circumcenter detection is technically sound.
+ The proposed method does not require exhaustive enumeration of traingle combinations or local surface parameterization, which are computationally expensive procedures.
+ The proposed method does not require any surface normal information, which are typically not available for a point cloud.
+ The proposed method is a single-stage pipeline and has high efficiency compared to exsiting two-stage pipelines.
+ The proposed method has achieved SOTA results in term of quality in the ABC dataset and MGN dataset, and highly competitive results in the FAUST dataset.
+ The proposed method has achieved SOTA results in term of efficieny among deep learning based methods.
+ The proposed method is robust to noisy and non-unifrom data.
+ The proposed method works for both water-tight and open meshes.

Weaknesses
- The anchor based detection approach is not novel but borrowed from existing object detection networks. Novelty is limited in this technical aspect.
- There is no evaluation on the effect of K (in KNN patch composition) and s (in detecting multiple circumcenters in a single anchor cell).
clarity,_quality,_novelty_and_reproducibility: Overall, this paper is clearly written and easy to follow. The idea of reformulating the point cloud trianguation problem into a circumcenter detection problem in a deep learning setting is original. The quantitative and qualitative results reported in the paper are both convincing and promising. There are also sufficient details included in the paper to reproduce the results.
summary_of_the_review: The idea of detecting circumcenters from point cloud patches and extracting traingles dual to the circumcenters is technically sound and novel. Sufficient evalations have been carried out to demonstrate the effectiveness, efficiency, and the robsutness of the proposed method. Experimental results show that the proposed single-stage pipeline outperforms existing two-stage pipelines in terms of reconstruction quality and efficiency. I would support accepting this paper.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: Problem: Given a cloud of 3D points, without associated surface normals, recover a triangle mesh.

Contribution: an interesting hybrid of ML model (GNN to predict "circumcenters" from local point networks) and conventional mesh manipulation.  Presents good results compared to other systems, and is a useful contribution to this subfield.  Reviewers agree that the idea is novel/original.

Weaknesses (post rebuttal):  It remains the case that the postprocessing is somewhat ad-hoc, and the relative contribution of the various components is hard to discriminate, either theoretically or empirically.


justification_for_why_not_higher_score: A relatively small subfield of interested practitioners.
justification_for_why_not_lower_score: Sufficient novelty, potentially useful within that subfield.
Decision: Accept: poster