MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction
Online HD map construction，vectorized representation，autonomous driving
Yes
No ethics review needed.
See the weakness.
1. The contributions are highlighted. The novel contributions compared with previous approaches are also discussed properly.
2. Both quantitative and qualitative results are shown and discussed. Ablation studies are conducted in a meaningful way.
This paper presents MGMapNet, a multi-granularity map network for end-to-end vectorized HD map construction based on multi-scale bird’s eye view (BEV) images. Evaluations on four datasets show the effectiveness of MGMapNet over multiple baseline models.
1. The citations of the whole paper are wrong. It should be \citep{} instead of \cite{}. 

2. From Figure 1 and 3. we can see the advantages of MGMapNet over other models. However, I can still see that the extracted lanes by MGMapNet are sometimes zigzagged while the ground truth lines are straight lines. I wonder whether you can add some regularity or loss terms to avoid this. Maybe for those straight lines, resample their vertices along the straight lines every times during model training so that the model learns the linear feature instead of individual point locations?

3. Can you describe each loss term in detail? Do you use the distance from each point to the ground truth points as the loss for L_{pts}?
Yes
No ethics review needed.
1.	Is it possible to consider instance-2-instance attention? Why not compare this?
1.	The problem studied in the paper is very important in practice and find applications in real world.
2.	The paper is clearly written and easy to follow. 
3.	Experiments are conducted to verify the performance of the proposed method.
The paper introduces MGMapNet, designed to effectively model map elements through a multi-granularity representation by integrating both coarse-grained instance-level and fine-grained point-level queries to enhance map modeling. The framework employs a Multi-Granularity Aggregator. Besides, there is a Point Instance Interaction module, which facilitates the exchange of information between the instance-level and point-level queries, thereby improving the overall modeling capability of the network.
1.	The challenges and contributions of the proposed techniques require further elaboration. What are the specific challenges to design these techniques in section 3? 
2.	The encoders and decoders are mostly MLP-based. It is difficult to understand the logic, rationale and difficulty to apply the techniques.
3.	Some evaluation metrics in experiments are not explained, e.g. AP_ped and AP_div, and AP_bou in table 1.
4.	How are the proposed techniques related to High-Definition?
5.	Quality of figures and tables can be improved. For example, Table 4 has too big font size.
Yes
No ethics review needed.
Please clarify the comments for W1-W3.
S1. The paper introduces a method that combines both coarse-grained instance-level and fine-grained point-level queries, effectively capturing both global category information and local geometric details of map elements. 

S2. The design of the Multi-Granularity Aggregator and Point Instance Interaction modules facilitates efficient and effective information sharing between instance-level and point-level queries.

S3. The proposed MGMapNet framework outperforms several baseline models, achieving the state-of-the-art performance in HD map construction.
The paper presents MGMapNet, a framework designed for end-to-end vectorized High-Definition map construction. MGMapNet introduces a multi-granularity representation that integrates both instance-level and point-level queries to effectively capture both category and geometric information of road elements. The proposed Multi-Granularity Aggregator and Point Instance Interaction modules allow information exchange between the two granularities, resulting in enhanced prediction accuracy. Experimental results demonstrate state-of-the-art performance on benchmark datasets such as nuScenes and Argoverse2, surpassing various baseline methods.
W1. The paper’s description can be overwhelming for readers who are not deeply familiar with the HD map construction topic (e.g., me). For example, it lacks a formal problem formulation, which would help in grounding the research context. Additionally, the method's explanation is a bit difficult to follow.

W2. The paper could be strengthened by providing a detailed analysis of the time and space complexity of MGMapNet compared to baseline models. Given that efficiency is a key motivation, understanding how MGMapNet performs in terms of computational and memory resources would be beneficial.

W3. It is not clear why the training epochs are set to have multiple values for various models, and why the long training schedule leads to fair comparison.
@inproceedings{
anonymous2024mgmapnet,
title={{MGM}apNet: Multi-Granularity Representation Learning for End-to-End Vectorized {HD} Map Construction},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=E8S5Upr6oO},
note={under review}
}
The construction of Vectorized High-Definition (HD) map typically requires capturing both category and geometry information of map elements. Current state-of-the-art methods often adopt solely either point-level or instance-level representation, overlooking the strong intrinsic relationships between points and instances. In this work, we propose a simple yet efficient framework named MGMapNet (Multi-Granularity Map Network) to model map element with a multi-granularity representation, integrating both coarse-grained instance-level and fine-grained point-level queries. Specifically, these two granularities of queries are generated from the multi-scale bird's eye view (BEV) features using a proposed Multi-Granularity Aggregator. In this module, instance-level query aggregates features over the entire scope covered by an instance, and the point-level query aggregates features locally. Furthermore, a Point Instance Interaction module is designed to encourage information exchange between instance-level and point-level queries. Experimental results demonstrate that the proposed MGMapNet achieves state-of-the-art performance, surpassing MapTRv2 by 5.3 mAP on nuScenes and 4.4 mAP on Argoverse2 respectively.
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.
Online HD map construction，vectorized representation，autonomous driving
I certify that there is no acknowledgement section in this submission for double blind review.
/pdf/2aba0635b13d8f515645af8dfc314b07d0a0deda.pdf
applications to computer vision, audio, language, and other modalities
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.
MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction
ICLR 2025 Conference Submission
ICLR.cc/2025/Conference/Submission