ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction
Authors: Jingyi Yu
Zizhao Zhang
Shengfu Xia
Jizhang Sang
Venue: CoRL 2023 Poster (2023)
Keywords: Map Construction
Multi-view Perception
Long-range Perception

Abstract: We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons to represent map elements, is widely used by downstream tasks. However, previous schemes designed with reference to dynamic object detection overlook the structural constraints within linear map elements, resulting in performance degradation in long-range scenarios. In this paper, we exploit the properties of map elements to improve the performance of map construction. We extract more accurate bird’s eye view (BEV) features guided by their linear structure, and then propose a hierarchical sparse map representation to further leverage the scalability of vectorized map elements, and design a progressive decoding mechanism and a supervision strategy based on this representation. Our approach, ScalableMap, demonstrates superior performance on the nuScenes dataset, especially in long-range scenarios, surpassing previous state-of-the-art model by 6.5 mAP while achieving 18.3 FPS.

ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction
Jingyi Yu School of Geodesy and Geomatics Wuhan University
Zizhao Zhang
Electronic Information School
Wuhan University
j jingyiyuEshu. edu. cn
3zair19970gmail com
Shengfu Xia
Jizhang Sang
School of Geodesy and Geomatics
School of Geodesy and Geomatics
Wuhan University
Wuhan University
xiashengfu@shu.edu.cc edu.ccn
sangjzh@whu. edu.
Abstract: We propose a novel end-to-end pipeline for online long-range vector- ized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons rep- resent map elements, is widely used by downstream tasks. However, previous schemes designed with reference to dynamic object detection overlook the struc- tural constraints within linear map elements, resulting in performance degradation in long-range scenarios. In this paper, we exploit the properties of map elements to improve the performance of map construction. We extract more accurate bird's eye view (BEV) features guided by their linear structure, and then propose hier- archical sparse map representation further leverage the scalability of vectorized map elements, and design progressive decoding mechanism and supervision strategy based on this representation. Our approach, ScalableMap, demonstrates superior performance on the nuScenes dataset, especially in long-range scenarios, surpassing previous state-of-the-ar model by 6.5 mAP while achieving 18.3 FPS. Code is available at https://gituub.com/jiii hwam//jnggyy/SSallabbaaa yu/ScalableMap.
Keywords: Map Construction, Multi-view Perception, Long-range Perception
Introduction
To ensure the safety of autonomous vehicles on the road, downstream tasks such trajectory pre- diction and motion planning typically rely high-definition (HD) maps prior information [1, 2], which provide centimeter-level location information for map elements. However, the production of such HD maps is generally carried out offline, involving complex processes known for their high labor and economic costs, making it difficult to construct maps that cover a wide area [3, 4].
Recent researches aim to construct local maps in real-time using on-board sensors. More studies show the superiority of schemes based bird's seyee view (BEV) representation for unifying data from various sensors. Early attempts [5, 6, 7, 8, 9] regard map construction tation task, using convolutional neural networks (CNN) to obtain occupancy grid map. However, these schemes can only generate rasterized maps. which lack instance and structure information about map elements and are therefore difficult to apply directly to downstream tasks [10].
semantic segmen-
HDMapNet [11] uses time-consuming heuristic post-processing algorithms generate vectorized maps. More recent approaches [12, 13] focus constructing end-to-end networks similar to dy- namic object detection schemes, treating map elements ordered sets of vertices. However, the properties of map elements are different from those of dynamic objects. Map elements are typically
7th Conference Robot Learning (CoRL. 2023), Atlanta, USA.

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linear and often parallel to axes [14], which makes it difficult to define bounding boxes. Moreover, in dense vehicle scenarios, the limited visibility of vertices with map elements in the image space hin- ders accurate map shape inference solely based on heatmaps. While recent approach [15] proposes hierarchical query embeddings to better describe the arbitrary shape of an element by modeling each vertex as query, requires dense points to ensure the shape of elements and need to predict large number of vertices simultaneously without structural guidance. This poses challenges to the conver- gence speed and performance, particularly in long-range scenarios. Therefore, there is still need for approach that can effectively capture the structural constraints within map elements to achieve high accuracy in long-range HD map construction tasks.
In this paper, we aim to exploit the structural properties of vectorized map elements to address the challenges of accurately detecting map elements longer ranges. First, we extract position-aware BEV features and instance-awand BEV features via two branches respectively and fuse them under the guidance of linear structure to get hybrid BEV features. Next, we propose a hierarchical sparse map representation (HSMR) to abstract map elements in sparse but accurate manner. Integrating this representation with cascaded decoding layers proposed by DETR [16], we design a progressive decoder to enhance the constraints of structured information by exploiting the scalability of vector- ized map elements and a progressive supervision strategy to improve the accuracy inference. Our scheme, ScalableMap. dynamically increases the sampling density of map get inference results at various scales, allowing to obtain more accurate map information faster.
Contributions. Our contributions are summarized as follows: (i) We propose ScalableMap, a first end-to-end long-range vectorized map construction pipeline. We exploit the structural properties of map elements to extract more accurate BEV features, propose HSMR based on the scalable vectorized elements, and design progressive decoder and supervision strategy accordingly. All of these result in superior long-range map perception.
(ii) We evaluate the performance of Scal- ableMap on the nuScenes dataset [17] through extensive experiments. Our proposed approach achieves state-of-the-art results in long-range HD map learning, surpassing existing multimodal methods by 6.5 mAP while achieving 18.3 FPS.
2
Related work
Lane Detection
The lane detection task has been popular research topic for many years. Early approaches to these tasks [18, 5] usually rely on segmentation schemes that require complex post- processing to obtain the final result. In order to obtain structured information, some schemes [19, 20] aim to find a unified representation of curves, while others [21, 22, 23, 24] utilize anchor- based schemes abstract map elements with open shapes. Compared with the above solutions, our thinking is closer to HRAN [4], which directly outputs structured polylines. However, relies on recurrent network that is known to be computationally inefficient. Our ScalableMap is capable of handling real map elements with complex geometric structures, while the previously mentioned methods can only handle a single type or regular shape.
Boundary Extraction Boundary extraction aims to predict polygon boundaries for object in- stances on images. Polygon-RNN [25, 26) adopts recurrent structure to trace each boundary se- quentially, which is not suitable for scenarios with real-time requirements. Some works [22, 27, 28] achieve good results in boundary extraction, but they are generally designed for polygons in image space and are not suitable for map construction tasks. The closest to our proposed scheme is Bound- aryFormer [29]. which uses queries to predict vertices of polygons obtain vectorized polygon boundaries. However, the differentiable loss it defines for closed-shape elements in image space is not suitable for map element that is dominated by open-shape linear elements, as they have less concentrated features compared to dynamic objects.
Vectorized HD Map Construction Recent work tries to infer vectorized HD maps directly from on-board sensor data. HDMapNet [11] generates vectorized maps using time-consuming heuristic

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Overview of ScalableMap. (a) Structure genidede-guided hybrid BEV feature extractor. (b) Hierar- chical sparse map representation Progressive decoder. (c) Progressive supervision.
Figure I:
post-processing method, while VectorMapNet [12] proposes two-stage framework with an end-to- end pipeline using slow auto-regressive decoder to recurrently predict vertices. InstaGraM [13] proposes graph modeling approach based on vertex and edge heatmaps to reason about instance- vertex relations, which may be difficult to infer some vertices of map element appeared in multiple views. Given the challenge of dealing with arbitrary shapes and varying numbers of vertices in elements, MapTR [15] tackles this by employing a fixed number of interpolations to obtain uni- form representation. But MapTR's hicrarchical query design primarily focuses on the structural association of elements during the initialization phase, resulting slow convergence and deterio- rating performance the perception range increases. Only SuperFusion [30] relevant work for long-range vectorized HD map construction, which also uses post-processingg to obtain vector- ized results. Our model is the first end-to-ens scheme that utilizes the structural properties of map elements throughout the entire process to construct long-range vectorized maps.
Methodology
3.1 Overview
Given a set of surround-view images {L1..., Ix) captured from & on-board cameras, the goal of ScalableMap to predict M local map elements j 1.... M) within a certain range in real-time, including lane dividers, road boundaries, and pedestrian crossings. Each map el- ement is represented by sparse set of ordered vertices, which
be described I,0) {000, yo, 20), ..., (Im, Im, 1 2m, }) where my is the number of vertices of element LG) and (z, y, 2) are the coordinates of each vertex in unified vehicle coordinate system.
The architecture of ScalableMap is illustrated in Figure 1. We build the model with three com- ponents to construct long-range vectorized HD maps: (1) piructure-uuider hybrid BEV feature ex- tractor: transforming camera sensor data into BEV features with structure-guided fusion (Section 3.2) (2) progressive decoder: layer by layer map element decoding based the proposed HSMR (Section 3.3): (3) progressive supervision: bipartite matching and training for HSMR (Section 3.4).
3

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3.2 BEV Feature Extractor
The ill-posed nature of 2D-3D transformation is exacerbated by the elongated and linear character- isties map elements, leading to feature misalignment and discontinuity. To obtain hybrid BEV features, we utilize one branch for extracting position-aware BEV features and another branch for extracting instance-aware BEV features. These branches are then fused together, guided by the structural properties map elements.
Perspective View Converter. We start by extracting image features through ResNet. Method proposed by BEVFormer [31] adopted obtain position-aware BEV features Flev. which utilizes deformable attention [32] to enable spatial interaction between BEV queries and corresponding image features based on predefined 3D grid and calibration parameters. Additionally, we several multi-layer perception (MLP) layers to obtain instance-aware BEV features Fbee since they are effective at preserving continuous features in image space [33]. k image features are individually converted to their respective top-views using & MLPs. To further improve feature continuity across views, we use a linear layer to transform top-view features into a unified BEV feature.
Strieture-Guided Feature Fusion. To enhance the robustness of features for accurate map con- struction, we propose a mutual correction strategy that leverages information from two distinct fea- tures: Feee with relatively precise positional data for select map vertices, and Fire encompassing comprehensive shape information for map elements. By directly summing these features, we pro- duce the updated F bev. Additionally, we introduce segmentation head to Fie.. guiding it to focus on the drivable area to learn the transformation scale. Subsequently, Pev is concatenated with the refined Fi... and their fusion executed through a convolutional layer. This fusion process corrects misalignments in hew. producing an hybrid BEV feature with enhanced richness and accuracy.
3.3 Progressive Decoder
The varied shapes of vectorized map elements present challenges for conventional abstraction schemes like bounding box-based and anchor-based approaches. To address this, we introduce HSMR as the core idea of our approach. HSMR provides sparse and unified representation that accurately describes the actual shape of elements while supporting fast inference. Building upon this, we design progressive decoder inspired by the DETR paradigm. Moreover, we incorporate module that generates structural queries first and then dynamically inserts queries, acting as a vital bridge to connect maps of different densities.
Hierarchical Sparse Map Representation. Polyline representations of map elements are typi- cally obtained by sampling points where the curvature exceeds a threshold, thus resulting in varying numbers of vertices for each element. We define the number of vertices forming each element as the map density to ensure consistent representation. Based on this density, we employ uniform point sampling for elements with an excessive number of vertices, while for elements with fewer vertices than the desired density, we perform point subsampling based distances between the original ver- tices. This approach allows to obtain representations of the same element at arbitrary densities. By combining the iterative optimization idea of DETR paradigm with the dynamically adjustable density of the vectorized map, we hierarchically utilize a low-density map as an abstract representa- tion of the high-density map. The low-density map captures map element shapes adequately while being sufficiently sparse. visual depiction of HSMR and its performance is provided in Figure 4.
Decoder Layers. We define query Jn.m responsible for the m-th vertex the n-th element. Lever- aging the hierarchical sparse representation of map elements, a small number of queries are initially generated to capture the approximate shape of each map element. Each query formed by adding an instance embedding inn and position embedding goos Our progressive map element decoder is composed of multiple decoder layers, each containing two types of attention mechanisms. These at- tention mechanisms facilitate information exchange among vertices, and enable interaction between

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Layer
Layer
Layer
Layer 6
Prodicted polyline
Ground polyline
Newly added vertices
Loss
Edge
Figure Visualization of progressive polyline loss.
each vertex and its corresponding BEV feature. The exchange between vertices is implemented ing multi-head self-attention [34], while the other is implemented using deformable attention [32].
Structural Query Generation and Dynamic Query Insertion. To connect layers that handle different densities, we exploit the positional constraints among adjacent vertices within the same element to augment the map density. We introduce new queries by taking the mean value of two adjacent queries that share an edge, and dynamically insert new queries between these two queries. Rather than employing traditional methods that initialize large number of queries simultaneously and update them iteratively, we adopt strategy of initializing each element with only limited num- ber of queries and gradually increasing the map density layer by layer. This enables the module to focus the original sparse instance features and leverage the structural characteristies of vectorized map elements, ensuring robust long-range perceptual capabilities.
3.4 Progressive Supervision
During training, we infers N map elements [ in each layer, and N is set to be larger than the typical number of elements in a scene. Assume there are targets {L') l==1> (L' which is padded with @ to form a set of size N. Following [16, 35J, bipartite matching is employed to search for permutation Σ with the lowest cost. Σ includes the equivalent permutation for each element, as multiple vertex orders can represent the actual shape of an element in map construction task:
o" = argmin DE
Po(cs))) + [ec Cmatch (Lo(t)) Le)l
(I)
where Po(o (c:) is the probability of class for the prediction with index (ii) and Lmatch is pise polyline matching cost between prediction Lo(s) and ground truth L,. We use Hungarian algorithm [36] find the optimal assignment a². We employ focal loss supervise the element category and drivable area, and additional loss terms are incorporated in the following loss function:
Cpolyline AyCvertex + Ledge
Vertex Loss. Considering HSMR involves subsampling process, we differentiate the supervision between original vertices and newly added vertices. A visual representation of the supervision mech- anism for the progressive polyline loss is shown in Figure 2. For each of predicted original vertex (o-(1) assigned to vertex Vij. employ LI distance to ensure prediction accuracy. With N standing for the number of original vertices in layer I, vertex loss of each element is formulated as:
N
N |10. Va
Lvertew Σ
I (( Σ
Edge Loss. We use edge loss supervise edge shape, which includes distance from newly added vertices (( Ig=1 N corresponding to the current edge eij, edge slope, and angle formed by adjacent edges. The distance component is supervised with LI loss, while the slope and angle
5

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Table 1: Results on nuScenes validation dataset. C denotes camera only and C+L denotes camera- LiDAR fusion. Range represents the perceived range along the Y-axis. FPS of Maa is mea- sured on single RTX 3090 GPU, with batch size I and GPU warm-up. The metrics of MapTR* are obtained by retraining the model while modifying only the perception range, following the of- ficial code and ensuring consistency with the claimed specifications. Metric values marked with represents the AP value under a threshold of 1.0. Since SuperFusion only provides this metric, we conduct the same benchmark test for fair comparison.
Method
Modality
Range
AP ped
AP divider
AP howndary
mAP
FPS
HDMapNett
C
[-30.0, 30.0]
14.4
21.7
33.0
23.0
0.8
HDMapNet
C+L
[-30.0, 30.0]
16.3
29.6
46.7
31.0
0.5
VectorMapNee
C
[-30.0, 30.01
36.1
47.3
39.3
40.9
2.9
VectorMapNet
C+L
[-30.0, 30.0]
37.6
50.5
47.5
45.2
InstaGraM
C
[-30.0, 30.0]
47.2
33.8
44.0
41.7
17.6
MapTRR
C
[-30.0, 30.0]
56.2
59.8
60.1
58.7
11.2
ScalableMap
C
[-30.0, 30.0]
57.3
60.9
63.8
60.6
18.3
MapTR**
C
[-60.0, 60.0]
35.6
46.0
35.7
39.1
11.2
ScalableMap
C
[-60.0, 60.0)
44.8
49.0
43.1
45.6
18.3
SuperFusion
C+L
[0.0, 60.0]
22.34
30.31
53,47
35.34
ScalableMap
C
[0.0, 60.0)
51.0
55.11
48.4f
51.5t
18.3
components are supervised with cosine similarity. The edge loss of each element is formulated as:
N
No. -1
N₂
L. Σ
A
Σ c(āo-((). {zs)}
k=0
where ((),) is the edge formed by two adjacent vertices, (g-()). is the angle formed by two adjacent edges, d(û, e) denotes the distance from vertex edge and c(ā, a) denotes the cosine similarity between two edges. No. is the number of added vertices corresponding to edge ex*
4 Experiments
4.1
Experimental Settings
Dataset and Metrics. We evaluate ScalableMap on the nuScenes dataset, which consists of 1000 scenes. Each scene has a duration approximately 20 seconds. The dataset provides 360-degree field of view around ego-car, captured by six cameras. Following previous works [12, 13, 15], use the average precision(AP) metric to evaluate the performance, and chamfer distance to determine which positive matches the ground truth. We calculate the AP for the three categories, and for each category, AP is computed under several thresholds (0.5, 1.0, 1.5).
Implementation Details. We train ScalableMap for 110 epochs on RTX 3090 GPUs with batch size 32. The perception range for regular range test [-15.0m, 15.0m) along the X- axis and [0.00m, 30.0m] along the Y-axis, and the perception range for long-range test is ex- panded to -0.00m, 60.0m) along the Yaxiis To unify the representation of vertices, Ramer-Douglas-Peuucee
the
algorithm [37] to simplify the original polyline with a threshold of 0.05m
before subsampling. For training, we set the loss scales dets as 2.0, X Ap as 5.0 and Åo A as 5e respectively. The progressive decoder is composed of six decoder layers.
4.2 Results.
Comparison with Baselines. We evaluate the performance of ScalableMap by comparing it with that of state-of-the the-art methods on nuScenes validation test. As shown in Table 1, under camera modality, ScalableMap performs slightly better than MapTR, achieving 1.9 higher mAP and faster

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LM J0
TAY
Figure 3: Visualization of qualitative results of ScalableMap in challenging scenes from nuScenes validation dataset. The left column is the surround views, the middle column is the inference results of the ScalableMap, the right column is corresponding ground truth. Green lines indicate boundaries, red lines indicate lane dividers, and blue lines indicate pedestrian crossings.
inference speed within the conventional perception range of [-30.0m 3m.0000....mmmmmmm 30.0m] along the Y-axis. When the same models are directly applied to [-60.0m, 60.0ml scenario, ScalableMap achieves 45.6 mAP and 18.3 FPS, while MapTR's 's corresponding values are 39.1 and 11.2. is noted that SuperFusion is the only method which publishes experiment results in this range. However, it is fusion model of lidar and single-view camera. The mAP achieved by our approach is higher than that of SuperFusion by 16.2 under the same benchmark. demonstrating the superior performance in multi-view camera modality with near real-time inference speed. The results demonstrate that our scheme effectively meets the real-time requirements of online map construction tasks, delivering superior accuracy in both conventional perception range tests and long-range tests.
Qualitative Results Visualization. The visualization of qualitative results of ScalableMap on nuScenes validation dataset in long-range test is shown in Figure More visualization results of challenging scenarios are presented in Appendix B for more visualization results of challenging scenarios. Our model still performs well even curved roads, intersections, congested roads, and night scenes. We further visualize three out of six decoder layers of MapTR* and ScalableMap in Figure 4. Our strategy demonstrates faster ability to focus the instance features, while the progressive iteration yields more precise shapes of elements.
7

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CAM_FRONT PRONT
CAM BACK
Layer
Layer
CAM FRONT LEFT
CAM FRONT RIGHT
Layer
Layes
CAM BACK
RIGHT
Layer
Layer
Surround views
MapTR*
ScalableMap
Figure 4: Visualization of prediction from three decoder layers of MapTR* and ScalableMap. The perception range along the Y-axis is [-60.0m, 60.0m]. The light-colored lines on the image repre- sent ground truth, while the dark-colored lines represent the inference results.
4.3 Ablation Studies
We conduct ablation experiments nuScenes validation set to verify the effectiveness of the com- ponents of the proposed method and different design. Settings of all experiments are kept the same as mentioned before. Additional ablation experiments are provided in Appendix A.
Table 2: Ablations about modules.
Ablation of Proposed Components. Table 2 presents
experimental results showcasing the impact of our pro- posed components. HSMR demonstrates effective per- formance in long-range perception with sparse repre- sentation. SQG&DQI enhances structural information within map elements, while the SGFF module signifi- cantly enhances performance.
HSMR SQG&DQI SGFF
mAP
40.1
39.7
42.6
45.6
Ablation of Number of Vertices. Ablations of the effect of number of vertices forming each element on long-range perception in each decoder layer are pre- sented in Table 3. The experimental results show that, based on our proposed HSMR, the model performance is quite stable with the number of vertices. We trade-off accuracy and speed to select the appropriate parameters.
Table 3: Ablations about vertex number.
Number of Vertices
mAP
FPS
2/3/5/9/9/9
43.6
19.2
3/5/9/17/17/17
45.6
18.3
4/7/13/25/25/25
44.2
17.6
5 Discussion
We propose ScalableMap, innovative pipeline for constructing long-range vectorized HD maps. We exploit the inherent structure of map elements to extract accurate BEV features, propose the con- cept of HSMR based on the scalable vectorized maps, and design progressive decoder and supervi- sion strategy accordingly to ensure fast convergence. Through these designs, our method effectively captures information over long distances. Experiment results on nuScenes dataset demonstrate com- pelling performance, particularly long-range scenarios, thus affirming its real-time applicability and effectiveness real-world environments.
Limitations. Our method relies solely on real-time camera sensor data, thus its performance de- pends the visibility of the scenarios, which may be limited in situations like traffic congestion or extreme weather conditions. Additionally, accurate camera calibration parameters are assumed, which can pose a constraint in practical deployment. Future research can focus on reducing the reliance on calibration parameters by developing calibration-free approaches or incorporating on- line calibration methods. Exploring the integration positional constraints among map elements or leveraging global coarse maps as prior knowledge may further enhance the robustness and accuracy.

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Acknowledgments
We appreciate the reviewers for their comments and feedback.
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A Ablation Study
A.1 The Way of Feature Fusion.
Given that SGFF employs mutual correction strategy, we conduct ablation experiments to validate the efficacy of this feature fusion approach. Specifically, we consider two scenarios: first, without correcting the position-aware features, where the two features are directly combined for fusion; and second, without correcting the instance-aware features, where the two features are directly fed into the convolutional layer for fusion. The result of these experiments robustly underscore the effectiveness of SGFF.
Table 4: Ablation studies feature fusion approaches.
Fusion Method
Ped Crossing
Divider
Boundary
mAP
w/o position-aware feature correction
39.9
43.7
38.6
40.7
w/o instance-aware feature correction
42.3
46.0
39.0
42.4
SGFF
44.8
49.0
43.1
45.6
A.2 Effectiveness of Edge Loss
Edge loss in ScalableMap comprises three crucial elements: the loss associated with newly intro- duced vertices concerning their corresponding edges, the loss concerning edge slopes, and the loss encompassing the angle formed by three consecutive vertices. The first element holds particular significance as it directly influences the shape regression of the map element, while the latter two el- ements exert their influence indirectly on the map element's shape. We conduct ablation experiments to underscore their efficacy the context of map construction tasks.
Table 5: Ablation studies on edge loss.
Loss Item
Ped Crossing
Divider
Boundary
mAP
w/o edge loss
43.9
47.6
42.7
44.7
with edge loss
44.8
49.0
43.1
45.6
A.3 Influence Vertex Count on Convergence Speed
Figure illustrates the convergence curves of our model in three abla- tion experiments conducted under different vertex configurations for long-range tests. Excessive vertices hinder convergence, whereas insufficient counts can compromise the accuracy of shape representa- tion. By carefully fine-tuning the number of vertices through abla- tion experiments, we strive to strike balance. This fundamental ap- proach aligns closely with the nu- anced perspective presented in our paper, further strengthening the ro- bustness of our findings.
2/23/993
407155/2/7755
0.1
60
50
100
Ebocho
Figure 5: Visualization of convergence curves.
12

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B Qualitative visualization
We present visual results of ScalableMap operating in adverse weather conditions and dealing with occlusion scenarios on nuScenes validation set as shown in Figure Figure and Figure 8.
DRU SCAI
CAU Jibtts of
GML PRONT
GAM JAOK
GAI WOL
CAN BACK
CAM EACK TIGHT
FAR TINIE THTHT
Figure 6: Visualization of qualitative results of ScalableMap in rainy scenes from nuScenes valida- tion dataset. The left column is the surround views, the middle column is the inference results of the Maa, the right column is corresponding ground truth. Green lines indicate boundaries, red lines indicate lane dividers, and blue lines indicate pedestrian crossings.
13

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11
HUM?
CALOKO DOTT
GM SACE
CAN LACK TEATT
CHU FRINT LOFT
CAN PLCNT
CAM PRONT SHT
GULMMENT 1997
DPN PROKET
CAR MONV CNT
GA MMOK UST
CMM RACE
CAM BACK SEGHT
Figure 7: Visualization of qualitative results of ScalableMap in nightly scenes from nuScenes vali- dation dataset. The left column is the surround views, the middle column is the inference results of the Sca,ableMap, the right column is corresponding ground truth. Green lines indicate boundaries, red lines indicate lane dividers, and blue lines indicate pedestrian crossings.
14

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GMM S4CC
CAM CAIL TEAR
GH mOITT.TTOO LOT
GAM PRONT
MAASOO
REAPERY
OU PRINT
DPN PROKET
CAR PROVT 2NT
VVVVVI
ppllis Two
GAI BOOK LSST
GM RACK
CAM BACK 3IGRTT
Fusionor
Figure 8: Visualization of qualitative results of ScalableMap in occlusion scenes from nuScenes validation dataset. The left column is the surround views, the middle column is the inference results of the ScalableMap, the right column is corresponding ground truth. Green lines indicate boundaries, red lines indicate lane dividers, and blue lines indicate pedestrian crossings.
15


Reviews:
[{"review_id": "yBOpJLV2XU5", "reviewer": "Reviewer_3QMy", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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.\n\nThe 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.", "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:\n1) proper space utilization, where some explanations could have been shortened and others would have needed more details\n2) relatively weak evaluation section, slightly connected with space issues stemming from 1).\n\nI would suggest some reorganization of the writing, and a more detailed ablation, would make this paper a good addition to the conference.", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Good: Minor revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "Highly relevant to robotics but no hardware experiments", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "Concerning the iterative/progressive refinement: Table 3 shows a sudden mAP drop in the last row. What would explain that? For example, are higher vertex densities in the first stages posing more difficulty for the losses to converge? Also, since the last three progression rounds have the same number of vertices per element, have you seen a real necessity to keep the 6 stages? Unfortunately, there is no ablation on this.\n\nIn lines 196-198 you mentioned that you simplify the curves to unify the vertex representation. Can you explain what unification means in this context (i.e. different densities for different levels)? And do you do this only for training, or also when comparing against the GT? Also, do you think that the 5cm threshold could introduce noticeable errors?\n\nThe polyline loss seems interesting enough, but I see no clear benefit of the edge loss without any ablation study. Are there pathological cases where the vertex loss would fail to optimize towards the optimal configuration, and only the edge loss would provide enough signal to resolve a tie? Or is the idea to compensate for induced errors in GT resampling?", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "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.  \n\nI 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. \n\nIn 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).\n\nAlso, 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.\n\nAlthough 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.", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed."}, "extra_text": {}}, {"review_id": "nj6KAI8TZ3", "reviewer": "Reviewer_8yNs", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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.", "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. ", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Very Good:  Well organized and clear.", "potential_impact": "", "robotics_focus": "Highly relevant to robotics but no hardware experiments", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "My concerns are listed in the 'review' section. I would encourage authors to address my concerns. ", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "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.\n2. 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. \n3. In the normal experiment setting of vectorized map construction, ScalableMap shows a relative minor improvement over MapTR (1.9 mAP). \n4. 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)\n5. feature aggregation in SGFF is a bit arbitrary without sufficient ablation study. ", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed."}, "extra_text": {}}, {"review_id": "f92788YfTO", "reviewer": "Reviewer_f6RE", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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.\n", "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.", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Very Good:  Well organized and clear.", "potential_impact": "", "robotics_focus": "Highly relevant to robotics but no hardware experiments", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "- It is a little bit unclear what is the semantic segmentation head doing, how is it fusing position-aware and instance-aware embeddings. Are they aligned by semantics? It would be great if that particular paragraph can be expanded a bit.\n- Minor missing reference in section 4.1:  “Following previous works  [12?,15]”\n", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "- 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.\n- Quality: the proposed framework is technically sound, backed up by comprehensive evaluations and ablation study. \n- Clarity: the paper is well written, easy to follow.\n- Significance: the work is addressing an important yet crowded problem area. The significance is incremental but important and difficult.\n- Relevance: the online mapping problem is critical in the automotive industry. The paper is definitely on the right path.\n- Limitations: Discussion section clearly states the limitation of camera-only, occlusion, being calibration dependent,not using prior knowledge, etc.\n", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning."}, "extra_text": {}}, {"review_id": "TipBn0FBnm1", "reviewer": "Reviewer_Z2cB", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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.", "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.", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Good: Minor revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "Relevant but unlikely to deploy to hardware in near future", "quality_of_limitations": "Additional details required", "questions_for_rebuttal": "- Please provide some results on how your approach would work in an online map-preserving setting that retains + integrates previous predictions.\n- Could you please delineate/portray/plot a sample prediction that suffers from severe occlusions? I would expect a collapsing prediction. How would you circumvent this in general? Do you have any kind of uncertainty estimate that could help here? Imagine a downstream task that uses map predictions. Should it rather utilize the current and potentially flawed prediction, or should it stick with a previous one + transformation given odometry is available?\n- As this is a robotics-related conference, I would be interested in seeing some predictions under adverse weather conditions, if possible.\n- Will you publish your code as part of this work? We usually expect that in conferences such as CoRL.\n", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "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. \n\n\nStrengths:\n- 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.\n- 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.\n- The ablation study seems to clearly show the effects of different design decisions regarding the hierarchical representation.\n\n\nWeaknesses:\n\n- 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.\n- 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.\n", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed."}, "extra_text": {}}]

Author rebuttal:

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)