MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction

Authors: Jing Yang, Minyue Jiang, Sen Yang, Xiao Tan, Yingying Li, Errui Ding, Jingdong Wang, Hanli Wang
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Online HD map construction，vectorized representation，autonomous driving

Abstract:
The construction of vectorized high-definition 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 relationship between points and instances. In this work, we propose a simple yet efficient framework named MGMapNet (multi-granularity map network) to model map elements with 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 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 performances, surpassing MapTRv2 by 5.3 mAP on the nuScenes dataset and 4.4 mAP on the Argoverse2 dataset, respectively.

Decision: accept (poster)
Reviewer scores: 6, 6, 6

Reviews and discussion:
<thread>
creation date:
1730438219404

thread_type:
official_review

<thread_object>
creator: reviewer_pxzj

<content>
summary:
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.

strengths:
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.

weaknesses:
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.

questions:
Please clarify the comments for W1-W3.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730606129382

thread_type:
official_review

<thread_object>
creator: reviewer_cu8c

<content>
summary:
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.

strengths:
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.

weaknesses:
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.

questions:
1.	Is it possible to consider instance-2-instance attention? Why not compare this?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730695018434

thread_type:
official_review

<thread_object>
creator: reviewer_hewp

<content>
summary:
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.

strengths:
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.

weaknesses:
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}?

questions:
See the weakness.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732440117816

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Official Comment by Authors

comment:
We thank the reviewers for their insightful comments and for the positive feedback provided on the paper. 
We have uploaded a new version of the manuscript, incorporating reviewer suggestions and addressing the points raised, marking changes in blue text. In particular, we have included:

- Corrected the citation format throughout the paper. (Reviewer hewp)
- Added a detailed description of the loss function in the appendix. (Reviewer hewp)
- Added a detailed description of the technological challenges and our contributions. (Reviewer cU8c)
- Added a description of some evaluation metrics in the experiment. (Reviewer cU8c)
- Modified the chart formats for improved legibility. (Reviewer cU8c)
- Figure 2 has been reorganized for improved clarity and aesthetics. (Reviewer cU8c)
- Included more detailed efficiency comparisons in the appendix. (Reviewer pXZj)

We believe these revisions have strengthened the paper and look forward to further feedback. Below, we offer specific responses to the individual comments from each reviewer.
</content>
</thread_object>

</thread>


Metareview:
metareview:
The reviewers agree that the problem studied is important in practice, the paper is clearly written and easy to follow. and the experiments are extensively conducted to verify the effectiveness of the model. The reviewers also raised some issues on the unclear explanation on the motivations and contributions of the paper. The writing and more explanations on the figures should be also improved. Some typos should be careful corrected in the final version.