Venue: NeurIPS 2022 Conference

Reviews:

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 paper focuses on the problem of extracting or reconstructing mesh surface from raw point cloud. The key idea is to learn an unsigned distance function to progressively get to the real surface. The unsigned distance field is critical to deal with objects that are not watertight but with inner parts. The authors proposed a consistency aware loss to keep the consistency of the learned unsigned distance fields to avoid adversarial optimization. A surface extraction algorithm is also proposed to extract mesh surface from the learned unsigned distance function.
strengths_and_weaknesses: Strengths:
First, using unsigned distance function is critical and important to handle complicated object structures. They have demonstrated better performance on public dataset both visually and in numbers. 

Weakness:
From my understanding, this proposed method doesn't have potentials to handle any noise in the raw point cloud, which means they require a clean point cloud as input. But in real scenario, the raw point cloud is not noise-free.
Another issue is that the surface extraction algorithm is a bit tricky. The extraction is mainly controlled by computing the sign of cls(.) function, but how could we guarantee the gradient of the unsigned distance field is always accurate. 
Finally, the authors haven't presented any failure cases or any dicussion on when the proposed method would fail.
questions: I have some questions or confuse on some technical details:
1) Will the optimization fall into local minimum with the Chamfer distance Loss of Eq.(3)? If yes, then how would this local minimum affect the optimization? If no, why the global minimum is guaranteed?
2) what is the performance of directly extending Neural-Pull to unsigned distance field?
3) The authors have spent much efforts on designing the Progressive Surface Approximation, but I didn't see ablation study on this component which I think it is critical.

Also, what is the computational cost to learn the unsigned distance field?
limitations: The authors have mentioned one limitation or possible future work which is to have a coarse-to-fine divided grids. But they haven't clearly discussed the limitations and also they haven't demonstrated any failure cases. Please refer to the Weakness part about some of my thoughts on the limitations.
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 presents a method to mesh point clouds. It performs optimization on a single scene (without any training) . It claims 2 contributions:
1. a loss function and optimization strategy, which in my understanding is essentially the one presented in neural pull [26] for signed distance function used for unsigned distance function and symetrized. It is often refered to as a "consistency aware" / "field consistency loss" and as fighting "adversarial optimization" (which makes little sense to me)
2. a meshing strategy, which to me seems an adaptation of marching cube to unsigned distance function
I would say there is a 3rd contribution, which is not claimed in the intro but is a part of the method section, which is the progressive (i.e. 2 step in practice) surface approximation, even if the quantitative gains associated to it are small.
It presents results on several dataset that seem to improve state of the art
strengths_and_weaknesses: While I am not an expert of the area, the benefits of the proposed approach in term of results seem clear to me, which I think is the main strength of the paper. The proposed approach also seem to make a lot of sense and is quite simple.

I see several weaknesses in the paper:
1. I found the paper very hard to parse while the proposed approach is quite simple:
- this is particularly true for 3.1 and 3.2. I think this is written only for people who are very familiar with the 3 most related papers + has far too many forward and backward pointers. For example, in 3.1, before anything about the method has been explained (no loss function, nothing on optimization), there are results, comparisons with 3 baselines and discussion of the differences (l 126-151 and figure 2) I do not think it can make sense before the full paper has been read and understood. Similarly, l. 167 discusses results obtained when using equation 3 which is presented l. 185. If this was a journal submission this could easily be solved with a "major revision", for a conference paper this is much harder to trust the authors with a major rewriting of the paper...
- another thing that annoyed me is that I could understand none of what the paper was doing from the abstract and intro. Terms like "consistency aware" / "field consistency loss" and as fighting "adversarial optimization" are not explained while  they refer to very simple idea, and I think they are designed to impress but make little sense/are not adapted (not sure if it's the fault of the authors or if they re-use terms from other paper)

2. I am unsure what the real technical contribution are: 
- to me the first contribution, which is a big part of the method section (3.1 and 3.2), is actually a very small modification of neural pull [26]. I think this is not recognized enough in the paper and find that a very annoying issue.
- the progressive surface approximation seem novel but this is not claimed clearly, so I am unsure whether this might be following another paper
- the surface extraction seem to be a relatively simple adaptation of marching cube: if the authors agree, this again should be acknowledge much more clearly in the abstract, intro and 3.4
- to me, the real contribution is actually taking the previous small idea together and making them into a very effective algorithm, which could make for a great paper if only it was acknowledged better and each part explained much more simply. Unfortunately, this again put me on the verge of recommending rejection for a conference paper.

3. smaller concerns are associated with the experiments (which again I found in general convincing)
- since the output is a mesh I would like to see metrics related to meshes, not only point cloud. For example, it would be quite easy to measure normal distances (up to flip)
- I am confused by the "low confidence range" experiment (table 7) I guess the low confidence range should be understood as "in addition to sigma", so 0.9\sigma for  example actually means "between 1 and 1.9\sigma from the origin": is that right? If that's right, why not experiment with much smaller values (e.g. 0.1 and 0.5 sigma), and in any case with larger values (2 and 4 sigma)? That would make the trend much clearer. In any case, this should be better explained, a small figure (earlier, in the method section) could help

All in all, because I think the method makes sense and because (as far as I can judge not being an expert) the results seem very good and the ablation convincing, I would still tend to recommend accepting the paper, trusting the authors with a major rewriting. 

questions: Please address in details may questions in weaknesses 1 and 2, and explain how you will modify the paper to clarify it (if I am unconvinced with the answer on these points I am likely to change my rating)
limitations: yes
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposed a method for surface reconstructions by training a neural network to predict unsigned distance fields (UDF). The learned UDFs are consistent-aware, and can be trained without ground truth distance fields, point normals, or large scale training datasets. A high quality surface can be extracted from the gradient vector field of the learned UDFs. The paper has achieved appealing results compared to some of the state of the art algorithms.
strengths_and_weaknesses: 1) The paper carefully examined the current failure mode of the UDF approximation methods, thus proposing the consistency-aware field learning loss, and the progressive approximation paradigm. These strategies greatly improved the quality of the learned UDF, as illustrated by the paper.
2) Traditional marching cube algorithms cannot be directly applied on UDFs since there is no inside/outside information in an UDF. The paper proposed a novel surface extraction algorithm by looking at the gradient vector field of the learned UDF. From the originality and quality perspective, the paper has done well.
3) Presentation is well done, language and visualization are clear.
4) From a significance perspective, the reviewer believe the paper has boosted the SOTA by a quite large margin. The reconstructed surface has much higher quality in many challenging scenarios.
questions: 1) Are there plans to release source code or pretrained models to the community?
2) The paper did not talk much about the scalability of the proposed method. For example, it would be interesting to know if the proposed method can handle millions of points, city-scale LiDAR scans, etc? How much computation time/computation resources does the proposed method need?
limitations: The author addressed the limitations of uniformly dividing grids for surface extraction.
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 presents a framework to learn Unsigned Distance Functions (UDF) from point clouds. The learned continuous UDF can then be used to extract surface to represent 3D geometry. One of the challenges of learning a continuous UDF from a discrete point cloud is the instability of gradient due to the sparsity of points. To this end, the authors propose a novel loss function with a field consistency constrain. They also designed a progressive scheme to learn more local details. Unlike SDF that can recover surfaces using the Marching Cubes algorithm directly, UDF cannot pass the inside-outside test due to the lack of direction information (i.e., sign). Therefore, this paper propose to use the relative angle between query points to test whether they cross the iso-surface. Experiments demonstrate the proposed method outperform existing methods and ablation studies verify the design choices. 
strengths_and_weaknesses: Strengths:
- The paper is well written. It is easy to follow

- The figures are greatly helpful for readers to understand the idea.

- The proposed idea is interesting and effective as it is supported by the superior performance in comaprisons against existing methods. Furthermore, ablation studies are sufficient to validate design choices. 

Weaknesses:

- figure captions
I would recommend to expand figure captions so that readers don't need to jump back and forth between text and figure.

questions: N/A
limitations: N/A
review_title: null

Meta-review: metareview: All reviewers were clearly in favor of accepting the paper pre-rebuttal. There was limited discussion post-rebuttal. The AC examined the paper, the reviews, and the authors' response and is inclined to accept the paper. The AC encourages the authors to use their extra page to incorporate their responses to the reviewers into the final version of the paper. In particular, the AC would encourage carefully considering the feedback on presentation from 1bdf. 
confidence: Certain
Decision: Accept