PolyhedronNet: Representation Learning for Polyhedra with Surface-attributed Graph
polygon;polyhedron;polygonal representation;representation learning;graph neural networks
Yes
No ethics review needed.
no further question
Novel rand effective representation, relatively novel redefinition of the message-passing over the representation. 
Very strong classification and retrieval tasks.
The paper is relatively well written, although in several places key concepts (such as the local rigid representation) are implicitly defined inline, while a separate formal definition would have made the read easier.
The paper presents  a novel graph representation for polyhedral shapes and a novel message-passing approach over this representation to perform polyhedral representation learning.
the proposed surface-attributed graphs maintains an hyperedge per face connecting the ordered boundary edges of said face, and then characterize said graph with a local rigid representation where the connectivity and metric structure is encapuled by the series of two-hop paths from each vertex.
The flow of information from one face to another is captured by splitting said paths into inter-face flows where the path changes face, and 
 intra-face flow, capturing information of a single face.
The work has its niche and works well within it.I don't see any clear weaknesses except perhaps the impact within and outside the niche.
Yes
No ethics review needed.
- As pointed out in Weakness 2, could you please specify how you modeled the face attributes?
- In the experiments on polyhedral digit recognition shown in Figures 4, how do face attributes effectively differentiate similar digits, such as "6" and "9"?
- In section 5.7, you mentioned that "The ability to discern different parts of objects through attributes like color is particularly effective in complex cases involving multi-part objects such as loudspeakers, knives, lamps, and benches, facilitating accurate feature assembly." However, from the ablation studies in Tables 3 and 4, it seems that the inclusion of face attributes does not significantly affect the ShapeNet dataset, but it severely affects the MNIST-C dataset. Why is that?
- PolyhedronNet introduces the novel concept of Surface-Attributed Graph for polyhedral representation learning, addressing the limitations of traditional graph representations that cannot capture the face attributes of polyhedra.
- The experimental results on four datasets are thorough, demonstrating superior performance in both classification and retrieval tasks compared to existing methods.
The paper proposes a framework named PolyhedronNet for effective representation learning of 3D polyhedron. By introducing the Surface-Attributed Graph, the method designs a Local Rigid Representation based on Graph Neural Networks and further develops PolyhedronGNN to aggregate these local representations, ultimately achieving a robust global polyhedral representation invariant to rotation and translation. Experimental results demonstrate significant performance improvements on four datasets, particularly in classification and retrieval tasks.
- Some definitions are expressed quite confusingly, making it difficult to accurately understand what the authors intend to convey. For instance, the notation ϕ_(i,j,k) in Equation 1 and the corresponding illustration in Figure 2(b), as well as the description of a_(j,i)and a_(k,j)in the calculation of g^((l))  in Equation 2.
- The article emphasizes the importance of modeling face attributes, but it does not clearly indicate how the attribute set a in the surface-attributed graph G=(V,E,F,a) is obtained.
- While the paper compares with several methods, it would be beneficial to include comparisons with more recent state-of-the-art methods in geometric deep learning.
- The ablation study is limited to the effect of face attributes. Further ablation studies on different components of the framework could provide more insights.
Yes
No ethics review needed.
1. I recommend adding additional baseline comparisons, such as HGT, HAN, and PolygonGNN, to provide a more comprehensive evaluation of PolyhedronNet's performance. Additionally, incorporating some datasets like DBSR for validation could enhance the robustness of the results.
2. The paper employs a sum operation for aggregating vertex features. It would be beneficial to provide experimental results for alternative aggregation methods, such as average and max, to examine their impact on model performance.
3. Could you conduct ablation experiments to compare the inner-face and intra-face geometric message passing modules? 
4. Please provide an analysis of the time complexity of the proposed methods. If the complexity is too high, are there any strategies or optimizations that could be implemented to address this issue?
5. Given that the experimental results demonstrate significant improvements, can you provide the open-source code?
1. This paper incorporates face attributes into the model, enhancing the understanding of geometric and semantic information and improving representation learning. 
2. The authors introduce two message passing mechanisms (inner-face and cross-face) that facilitate information flow within and between faces, strengthening local information aggregation and sensitivity to geometric relationships. 
3. Results across multiple datasets show that PolyhedronNet achieves notable performance gains in classification and retrieval tasks, validating the proposed methods and their applicability in 3D object representation learning.
The paper presents PolyhedronNet, a novel framework for representation learning of polyhedra using surface-attributed graphs (SAG). The authors aim to address the limitations of existing methods that predominantly focus on vertex sequences, failing to capture the intricate surface characteristics of 3D polyhedral objects. The proposed approach involves decomposing the SAG into local rigid representations, which effectively maintain geometric relationships while minimizing information loss. The authors introduce PolyhedronGNN, a graph neural network designed to hierarchically aggregate these local representations, achieving a global representation that is invariant to rotation and translation. Experimental results on multiple datasets demonstrate the effectiveness of PolyhedronNet in classification and retrieval tasks, significantly outperforming other methods
1. While the paper presents experiments on four datasets, the diversity of polyhedral object types is somewhat limited. I recommend that the authors include a broader range of datasets to enhance the generalizability of the results and to provide a more comprehensive evaluation of the model's performance.
2. The proposed PolyhedronGNN appears to be computationally intensive, especially when applied to large polyhedral datasets. It would be beneficial for the authors to discuss the model's scalability and potential optimization strategies for handling larger datasets efficiently, as this would greatly enhance its practical applicability.
Yes
No ethics review needed.
Please add experiments for comparison with PolygonGNN.
Please add experiments for comparison with modern point cloud methods, especially the works in recent years rather than just PointNet.
This design is very straightforward, easy to understand, and has been proven to be effective in the experimental part.
In this paper, the authors propose a novel method for  polyhedra representation learning. The design is straightforward and has been proven to be effective.
I am not an expert in this domain; however, I have an understanding of the author's design and experiments. My inquiries primarily stem from the following two aspects:
a)There is an absence of a comparison experiment with Reference [1] mentioned below. It appears that both are engaged in polyhedron representation learning.
b)What are the advantages of this face-based representation method in comparison to the point cloud-based representation method, particularly in numerical experiments? (Given that this paper explicitly mentions the shortcomings of the point cloud-based method.)

[1]Yu, D., Hu, Y., Li, Y., & Zhao, L. (2024, August). PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4012-4022).
A framework for encoding polyhedral objects using surface-attributed graph
@inproceedings{
anonymous2024polyhedronnet,
title={PolyhedronNet: Representation Learning for Polyhedra with Surface-attributed Graph},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=BpyHIrpUOL},
note={under review}
}
Ubiquitous geometric objects can be precisely and efficiently represented as polyhedra. The transformation of a polyhedron into a vector, known as polyhedra representation learning, is crucial for manipulating these shapes with mathematical and statistical tools for tasks like classification, clustering, and generation. Recent years have witnessed significant strides in this domain, yet most efforts focus on the vertex sequence of a polyhedron, neglecting the complex surface modeling crucial in real-world polyhedral objects.
This study proposes \textbf{PolyhedronNet}, a general framework tailored for learning representations of 3D polyhedral objects.  We propose the concept of the surface-attributed graph to seamlessly model the vertices, edges, faces, and their geometric interrelationships within a polyhedron. 
To effectively learn the representation of the entire surface-attributed graph, we first propose to break it down into local rigid representations to effectively learn each local region's relative positions against the remaining regions without geometric information loss. Subsequently, we propose PolyhedronGNN to hierarchically aggregate the local rigid representation via intra-face and inter-face geometric message passing modules, to obtain a global representation that minimizes information loss while maintaining rotation and translation invariance.
Our experimental evaluations on four distinct datasets, encompassing both classification and retrieval tasks, substantiate PolyhedronNet's efficacy in capturing comprehensive and informative representations of 3D polyhedral objects.
I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
polygon polyhedron polygonal representation representation learning graph neural networks
I certify that there is no acknowledgement section in this submission for double blind review.
/pdf/a38d1889b24ab3741bb43238e8522dcfd7d0b077.pdf
learning on graphs and other geometries & topologies
I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
PolyhedronNet: Representation Learning for Polyhedra with Surface-attributed Graph
ICLR 2025 Conference Submission
ICLR.cc/2025/Conference/Submission