Venue: CoRL 2023 Conference

Reviews:

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary_of_paper: The paper proposes ScalableMap, an online vectorized HD map prediction approach that leverages structure guidance and progressive decoding and supervision, targeting enhancing long range prediction quality. The approach extract position-aware features using BEVFormer and instance-aware features using MLPs, fusing them with semantic segmentation head, followed by a cascade of transformers for different resolution decoding and supervision. Evaluation on nuScens dataset shows that the approach beats state-of-the-art by 6.5mAP while maintaining a lightweight footprint, executing at 18FPS.

review: - Originality: the paper is well motivated, carving out the novel missing piece in the related literature, which is a structured prediction approach targeting long range map construction. Related work is widely surveyed and well cited.
- Quality: the proposed framework is technically sound, backed up by comprehensive evaluations and ablation study. 
- Clarity: the paper is well written, easy to follow.
- Significance: the work is addressing an important yet crowded problem area. The significance is incremental but important and difficult.
- Relevance: the online mapping problem is critical in the automotive industry. The paper is definitely on the right path.
- Limitations: Discussion section clearly states the limitation of camera-only, occlusion, being calibration dependent,not using prior knowledge, etc.

robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The paper is addressing an important problem of online long range mapping for autonomous vehicles. The proposed approach is evaluated better than existing state-of-the-art while maintaining a light footprint and high FPS.
review_title: null

final_score: rating: Weak Reject: I recommend rejecting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary_of_paper: This paper presents an online approach to learning scalable map representations for HD map construction with a particular focus on large sensing distances. The authors motivate their approach by showing how especially axis-parallel objects that might span multiple distinct camera views are hard to capture using standard object detection formulations in comparison to vectorized approaches. First, the authors follow BEVFormer for encoding surround view imagery into BEV features that are both instance- and position-aware. Next, a set of queries is generated to predict coarse map elements. A so-called progressive supervision scheme is proposed in order to turn coarse maps into fine-grained vector-based map representations. By averaging out existing queries per step, new mean queries increase the number of vertices and thus increase the map resolution per step. The authors evaluate their architecture on the NuScenes validation-set and outperform MapTR on two distinct evaluation ranges.
review: The authors propose an intuitive and logical approach to online map learning and do well at explaining the proposed transformer-based approach. The authors are able to clearly point out what differentiates their paper from others. 


Strengths:
- The authors present a promising approach and show how advances from transformer-based segmentation research can effectively be applied to the task of online map learning.
- I think the progressive supervision scheme is a very interpretable way of generating predictions and illustrates how real-time applications might benefit already from coarser representations.
- The ablation study seems to clearly show the effects of different design decisions regarding the hierarchical representation.


Weaknesses:

- I guess MapTR* was not fine-tuned for a different perception range. In contrast, I do not see any evidence that you did / did not use different hyperparameters for larger evaluation ranges.
- There is no comparison in terms of the number of parameters compared to MapTR. Since the successive decoding is your main contribution this needs a more thorough analysis. Since your results are rather incremental, how can we be sure its just not just because of a significantly larger modeling capability of the chosen network architecture in comparison to MapTR.

robotics_focus: Relevant but unlikely to deploy to hardware in near future
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: The paper is well-motivated and shows interesting contributions in the field of online HD mapping. My main doubts are with respect to the network size and its comparison to MapTR as well as some proper analysis of failure cases from a robotics perspective. I am willing to raise my recommendation if my concerns are addressed during the rebuttal stage.
review_title: null

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary_of_paper: The paper addresses the problem of camera-based onboard mapping by means of detecting vectorized road map elements. The authors propose a method that is based on three separate stages, where they 1) encode the visual features into BEV space with instance- and position-aware subnets, 2) have an attention-based progressive refinement of hierarchical representations of increasingly-resolved map elements, and 3) a DETR-inspired loss formulation to associate predicted map elements to GT map elements, and then minimizing the associated polylines via a loss formulation over vertices and edges.

The results on nuScenes, especially for long-range detection [-60,60] , show a significant improvement to related work, while enjoying some speedups. The authors also provide a (rather short) ablation study about the impact of individual components, as well as the impact of varying vertex densities at the 6 progressive stages.
review: Generally, I like the overall idea and premise of the paper of carrying the vectorized nature of the map elements throughout the pipeline. Also, I especially like the DETR approach of progressively decoding the output to higher vertex densities, where preceding stages provide rough alignment with subsequent refinement stages. I can easily imagine that this approach allows for good guidance and would help convergence.  

I think the paper provides some good novelty towards end-to-end on-board mapping, and the results are definitely convincing. Unfortunately, the authors could not find the necessary space to explain their method in sufficient detail, and I keep wondering about the implementation details of many different pieces. I also checked the supplement (which contains the source code), but I would have greatly appreciated some more in-depth explanations in a separate appendix. 

In the same vein, I think that the evaluation is generally weak. The comparison was conducted on a single dataset only, and the ablation provided only very little insight. I understand that the page limit is tight, but IMO the authors wasted too much space with Figure 1 (could be removed), and Figure 3 (could be shortened).

Also, Section 3.4 on Progressive Supervision, as well as the preceding section on the structural and dynamic queries, is not completely clear to me after multiple readings, and I suggest adding at least one figure that visualizes these various things.

Although the writing is overall clear, there are occasionally some missing words or slight grammatical mistakes. I would ask the authors to proof-read the Method section, and fix the cite issue on line 189.
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: Overall I am inclined to accept the paper, as it's a sound and properly-executed idea. The paper writing itself has some flaws, though:
1) proper space utilization, where some explanations could have been shortened and others would have needed more details
2) relatively weak evaluation section, slightly connected with space issues stemming from 1).

I would suggest some reorganization of the writing, and a more detailed ablation, would make this paper a good addition to the conference.
review_title: null

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary_of_paper: The authors propose ScalableMap, a new methodology aimed at leveraging the structural properties of vectorized map elements for accurate long-range detection. This approach includes the extraction of position-aware and instance-aware bird's-eye view (BEV) features, which are fused using linear structure guidance to yield hybrid BEV features. A important part of this method is a hierarchical sparse map representation (HSMR), allowing for accurate but sparse abstraction of map elements. When combined with cascaded decoding layers (DETR), a progressive decoder is designed to enhance structured information constraints, using the scalability of vectorized map elements and a progressive supervision strategy to increase inference accuracy.
review: 1. This paper works on a vibrant new research topic of vectorized HD map construction and presents two method improvements, namely structure-aware BEV encoder and progressive decoder, to increase the performance upon previous SOTA methods.
2. HSMR is an interesting design for map elements that always come in different densities. I suspect it would be very useful for map learning. However the ablation study is missing in Table 2. 
3. In the normal experiment setting of vectorized map construction, ScalableMap shows a relative minor improvement over MapTR (1.9 mAP). 
4. Little rationality is provided about why ScalableMap is much better in long range. I am wondering whether other SOTA methods would have a much better result in long range (e.g., InstaGraM, VectorMapNet)
5. feature aggregation in SGFF is a bit arbitrary without sufficient ablation study. 
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: Overall, I think it's an interesting paper working on an important problem with two model improvements. Some of the design choices seem a bit arbitrary for me, thus I would encourage the author to add more experiments.
review_title: null

Meta-review: This paper proposes a vectorized HD map prediction approach focused on improving the long-range prediction performance. The reviewers find the paper well written and well motivated, addressing an important problem. There were several concerns raised by the reviewers during the initial review, most of which were addressed in the rebuttal. A few minor concerns exist, e.g, didn't add model size comparison to the paper, although mentioned in the rebuttal.
Decision: Accept (Poster)