SafeMap: Robust HD Map Construction from Incomplete Observations
Authors: Xiaoshuai Hao
Lingdong Kong
Rong Yin
Pengwei Wang
Jing Zhang
Yunfeng Diao
Shu Zhao
Venue: ICML 2025 poster (2025)
Keywords: Autonomous Driving；HD Map Construction；Incomplete Observations；
Primary area: applications->computer_vision
TL;DR: Robust HD Map Construction from Incomplete Observations

Abstract: Robust high-definition (HD) map construction is vital for autonomous driving, yet existing methods often struggle with incomplete multi-view camera data. This paper presents SafeMap, a novel framework specifically designed to ensure accuracy even when certain camera views are missing. SafeMap integrates two key components: the Gaussian-based Perspective View Reconstruction (G-PVR) module and the Distillation-based Bird’s-Eye-View (BEV) Correction (D-BEVC) module. G-PVR leverages prior knowledge of view importance to dynamically prioritize the most informative regions based on the relationships among available camera views. Furthermore, D-BEVC utilizes panoramic BEV features to correct the BEV representations derived from incomplete observations. Together, these components facilitate comprehensive data reconstruction and robust HD map generation. SafeMap is easy to implement and integrates seamlessly into existing systems, offering a plug-and-play solution for enhanced robustness. Experimental results demonstrate that SafeMap significantly outperforms previous methods in both complete and incomplete scenarios, highlighting its superior performance and resilience.

SafeMap: Robust HD Map Construction from Incomplete Observations
Xiaoshuai Hao
Lingdong Kong² 2
Rong Yin
Pengwei Wang'
Jing Zhang
Yunfeng Diao
Shu Zhao
Abstract
Surround-view Cameras
Map Construction Visualization
Robust high-definition (HID) map construction is vital for autonomous driving, yet existing methods often struggle with incomplete multi-view cam- era data. This paper presents SafeMap, a novel framework specifically designed to ensure accu- racy even when certain camera views are miss- ing. SafeMap integrates two key components: the Gaussian-based Perspective View Reconstruc- tion (G-PVR) module and the Distillation-based Bird's ssd'ssEii s-Eye- View (BEV) Correction (D-BEVC) module. G-PVR leverages prior knowledge of view importance to dynamically prioritize the most informative regions based on the relation- ships among available camera views. Further- more, D-BEVC utilizes panoramic BEV features to correct the BEV representations derived from incomplete observations. Together, these compo- nents facilitate comprehensive data reconstruction and robust HD map generation. SafeMap is easy to implement and integrates seamlessly into ex- isting systems, offering a plug-and-plaz solution for enhanced robustness. Experimental results demonstrate that SafeMap significantly outper- forms previous methods in both complete and incomplete scenarios, highlighting its superior performance and resilience.
Incomplete
Observations
62.5
40.4
49.5
50.5
44.2
40.5
40
$1.2
30
20
C
IC-F
IC-FR
IC-FL
IC-B
IC-BL
IC-BR
Map Construction Performance under Complete/Incomplete Observations
Figure I. Online Vectorized HD Map Construction with Incom- plete Observations. C/IC denotes complete/incompplet observa- tions. IC-F/LL/FR/B/BLL// represents collection of six scenar- ios of missing camera views, where F, B, L, and R correspond to front, back, left, and right camera views, respectively.
1. Introduction
Online high-definition (HD) map construction is critical and challenging task in autonomous driving, providing pre-
cise and detailed static environmental information essential for vehicle planning and navigation (Caesar et al., 2020; Zhang et al., 2025). These maps enable ego-vehicles to accurately localize themselves on the road and anticipate upcoming features (Zhang et al., 2023; Ding et al., 2023; Qiao al.. 2023). HD maps encapsulate vital semantics, including road boundaries, lane dividers, and road markings (Liao et al., 2023a; Hao al., 2024a). Depending the input sensor modality, HD map construction models are typically categorized into three types: camera- based (Hao et al., 2025b; Yuan et al., 2024), LIDAR-based (Li et al., 2022a; Sauerbeck et al., 2023), and camera-LiDAR fusion models (Liao et al., 2023a; Hao al., 2024b; 2025a).
'Beijing Academy of Artificial Intelligence National Uni- versity of Singapore 'Institute of Information Engineering, Chi- nese Academy Sciences "School of Computer Science, Wuhan University "School of Computer Science. Hefei University of Technology 'Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) "Independent Researcher. Correspondence to: Yunfeng Diao <diaoyun- Eing@hfu.eedu.cm
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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SafeMap: Robust HD Map Construction from Incomplete Observations
Recently, multi-view camera-based HD map construction methods have gained attention due to advancements in Sird's-Eye--ieee (BEV) perception (Chen et al., 2024a; Ma et al., 2024). Compared to LiDAR- based and baseed approaches, multi-view camera methods are easier and more cost-effective to deploy (Li et al., 2022b; Liu et al., 2023c). However, a key limitation is their dependence on complete multi-view images, which can lead to catastrophic failures when views are obstructed by occlusions sensor mal- functions (Kong et al., 2023b; Li et al., 2024). As shown in Fig. 1, the absence of crucial visual information can significantly degrade overall map construction. Thus, it be- comes crucial to enhance the robustness of online vectorized HD map construction under incomplete visual observations. Overcoming these challenges essential for ensuring safe navigation, particularly in complex and extreme driving scenarios, thereby significantly contributing to the overall reliability of autonomous systems (Kong et al., 2024).
tive plug-and-play techniques that integrate seamlessly with existing pipelines. Experimental results demonstrate that SafeMap significantly outperforms previous methods across both complete and incomplete observations, showcasing superior performance and robustness.
The contributions of this paper are mainly three-fold:
We present SafeMap, a robust HD map construction framework that ensures high accuracy and reliability even in the presence of missing camera views.
We introduce two innovative techniques in SafeMap: 1) the Gaussian-based Perspective View Reconstruction module, which utilizes relationships among available camera views to infer missing information through Gaussian- based reference point sampling, and 2) the Distillation- BEV Correction module to further correct the BEV feature extracted from incomplete observations.
Enhancing the robustness of driving perception models against sensor failures has emerged as a prominent topic in autonomous driving research (Kong et al., 2023b;a; Liu et al., 2023a). MapBench (Hao al., 2024a; 2025c) eval- uates the reliability of HD map models against various real-world corruptions, revealing that sensor failures can severely compromise performance and traffic safety. Al- though recent methods like MetaBEV (Ge et al., 2023) and UniBEV (Wang et al., 2024) have started to tackle camera failures in 3D object detection, maintaining commendable performance even when camera views or LiDAR signals lost. However, these approaches still rely complete multi-view camera images and do not tackle the challenges of incomplete observations due to camera damage occlu- sions (Xie et al., 2023). Additionally, Chen (Chen et al., 2024b) introduced a masked view reconstruction module (M-BEV) to enhance robustness in 3D object detection un- der various missing view scenarios. Nonetheless, incom- plete multi-view camera-based methods for HD map con- struction remain underexplored.
SafeMap outperforms state-of-the-ae methods in both complete and incomplete scenarios, demonstrating su- perior performance and robustness, thereby establish- ing a strong baseline for HD map construction research.
2. Related Work
HD Map Construction. The construction of HD maps is a pivotal area of research in autonomous driving, with exist- ing methodologies categorized by their input sensor modal- ity into camera-based (Liu et al., 2024a; Li, 2024; Chen et al., 2025; Zhao al., 2025; Zhang et al., 2024), LiDAR- based (Li et al., 2022a; Liu et al., 2023b), and camera- LiDAR fusion (Dong et al., 2024; Liao et al., 2023a;b; Xi- aoshuai et al., 2025; Zhou et al., 2024) models. Among these, multi-view camera- based approaches are particularly favored for their ease of deployment and cost-effactiveness (Ma et al., 2024; Chen et al., 2024a). However, these meth- ods often rely on the availability of complete multi-view images, making them susceptible to failures when one or more camera views are compromised, potentially leading to traffic safety incidents (Kong et al., 2024). This limitation highlights the urgent need for robust solutions that can facili- tate accurate online HD map construction, even in scenarios where camera views are incomplete. Our research pioneers efforts to enhance robustness in multi-view camera-based HD map construction under conditions of view corruption or failure encountered in real-world environments.
To bridge this gap, we propose novel framework, SafeMap, designed to maintain accuracy even when cam- era views are missing. SafeMap comprises two key com- ponents: the Gaussian-based Global View Reconstruction (G-GVR) module and the Distillation-based BEV Correc- tion (D-BEVC) module. G-GVR utilizes Gaussian-based sparse sampling to generate strategic reference points based on view importance, serving as spatial priors for lost views. These reference points facilitate deformable attention, al- lowing the model to dynamically focus on the most infor- mative regions across available views and efficiently re- construct missing views. Meanwhile, D-BEVC leverages complete panoramic BEV features to further correct the BEV representations obtained from incomplete observa- tions. Importantly, these components are simple yet effec-
Robustness against Sensor Failures. Sensor failures sig- nificantly challenge the accuracy of perception systems, posing direct risks to the safety of autonomous vehicles. Consequently, developing robustness against these failures has become crucial research focus (Dong et al., 2023;
2

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SafeMap: Robust HD Map Construction from Incomplete Observations
Surcund vlow Cameras
Complete Features
incopppee PV Features
Peecovered PV Features
HD Map Prediction
D-BEVC
2D Encoder
RVM
G-PVR
PV-BEV Trantormation
Map Decoder
PV Feature Reconstruction Loss
BEV Feature
Correction
Figure 2. Overview of the SafeMap Framework. We first extract features from complete multi-viww camera images and efficiently transform them into a unified BEV space using view transformations. To simulate emergency scenarios involving camera failures, we employ Random View Masking (RVM) and recovery scheme. Specifically, we introduce novel Gaussian-based Perspective View Reconstruction (G-PVR) modulc and a Distillation- Dastllation-based Bird's "eydds--y--Vear View Correction (D-BEVC) module to reconstruct the missing view information. Finally, the reconstructed BEV features are processed by a map decoder and prediction heads for HD map construction.
Zhu et al., 2023; Kong et al., 2024; Song et al., 2024; Xie et al., 2025). The resilience of BEV perception has been extensively studied across various applications, including 3D object detection (Song et al., 2025; Liu et al., 2023c;
camera views, enabling the reconstruction module within the map encoder to predict features for the missing views during testing. The model architecture, as shown in Fig. 2. consists of four key components: the Map Encoder (Sec- tion 3.2), the cauusian-bbase Perspective View Reconstruc- tion (G-PVR) module (Section 3.3), the Distillation based BEV Correction (D-BEVC) module (Section 3.4), and the Map Decoder (Section 3.5).
Yin et al., 2024), semantic segmentation (Zhang et al., 2022; Zhou & Krähenbühl, 2022; Xu et al., 2024; Hong et al., 2024; Liu et al., 2024b; Kong et al., 2025; Xu et al., 2025), depth estimation (Kong et al., 2023c; Wei et al., 2023a; Ke et al., 2024), and semantic occupancy prediction (Wei al., 2023b; Tang et al., 2024). Notably, MapBench (Hao et al., 2024a) evaluates the reliability of HD map models under natural sensor corruptions, highlighting significant performance degradation linked to sensor failures.
3.1. Preliminaries
To ensure clarity in notation, we first define the symbols and concepts used throughout this paper. Our objective is to develop a novel framework for safe and robust HD map construction capable of processing both complete and incomplete multi-view camera images. This framework pre- dicts vectorized map elements in BEV space, specifically targeting three types of map elements: road boundaries, lane dividers, and pedestrian crossings. Formally, we denote a set of multi-view RGB camera images captured in perspec- tive view as / = {41 J2, ..... IN where N represents the number of camera views. Each image Ię is characterized as I R H W x3 with Heam and Wream denoting the image height and width, respectively.
Comparisons with Recent Works. Recent advancements, such as MetaBEV (Ge et al., 2023), UniBEV (Wang et al., 2024), and M-BEV (Chen et al., 2024b), have begun to tackle sensor failures in 3D object detection frameworks. For instance, MetaBEV (Ge et al., 2023) incorporates meta-BEV query and an evolving decoder to mitigate the impact of sensor failures, while UniBEV (Wang et al., 2024) is designed to improve robustness against missing modali- ties. Nevertheless, these approaches still rely on complete multi-view camera images and do not address challenges from incomplete observations due to camera damage or oc- clusions. Furthermore, M-BEV (Chen et al., 2024b) aims to reconstruct image features from neighboring views when specific camera sensors fail. However, HD map construction heavily depends on static environmental data captured by surrounding cameras, necessitating specialized methods to handle incomplete observations. To our knowledge, this work is the first propose novel approach for HD map construction that addresses the challenges of incomplete multi-view camera data.
3.2. Map Encoder
We build our Map Encoder based on the popular HD map construction method MapTR (Liao et al., 2023a), which consists of 2D feature extractor and a Perspective-Vin View (PV) to BEV transformation module. Specifically, we first use 2D feature extractor (He et al., 2016; Liu et al., 2021) to extract multi-scale 2D features from each perspective view
[21, .., IN', and outputs complete multi- view PV features From (Fr, Fiz.-.., Fix }. Then, we adopt 2D-to-BEV feature transformation module (Chen et al., 2022) to map the multi-view PV features 1 into BEV features From The BEV features can be denoted as F E x where H. W, C refer to the spatial height, spatial width, and the number of channels of feature
3. Methodology
SafeMap presents a robust HD map framework that aims to maintain accuracy even in the presence of missing camera views. During training, we randomly mask and reconstruct
3

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SafeMap: Robust HD Map Construction from Incomplete Observations
maps, respectively. To mimic the emergency situation of camera failure in the testing phase, adopt view masking and recovering scheme in the training phase. Specifically, we randomly mask certain 2D image feature, name the incomplete PV feature as PP = {Fr, Fimmak..... Fix }. and then initialize the PV around
Missing
Gaussian-based Reference Point Sampling
Fincom using the spatial feature cues
Deformable Allention
Decoder
3.3. Gaussian-based PV Reconstruction Module
Figure 3. Illustration of the proposed Gaussian-basce Perspective View Reconstruction (G-PVR) module.
The challenge of reconstructing a missing view from mul- tiple perspectives is critical for ensuring comprehensive environmental perception. Existing approaches rely on crop- ping partial pixels from the left and right adjacent views and processing them through a transformer- for missing view reconstruction. While effective, it is con- strained by the need for predefined crop ratio and fails to fully leverage information from all available views, poten- tially overlooking valuable contextual cues.
the panoramic perspective view:
Ps ~ N(Na * W/2; Py ~ U(,
(2)
model
where N and U is the Gaussian distribution and uniform distribution, respectively. o² is the variance.
G-PVR module effectively guides the module in focusing on the most informative regions of the input views, and can easily adapt to different numbers of input views and varying spatial relationships between views. Through the deformable attention module with Gaussian- bassd reference points and the panoramic perspective view, the learnable query V aggregates the information from available views according to Eq. (1). Then, we use MAF-like transformer blocks to reconstruct the missing view:
To address the aforementioned limitations, we introduce novel Gaussian- based Perspective View Reconstruction (G- PVR) module, consisting of a learnable query vector that serves as flexible representation of the missing view cou- pled with multi-view values derived from all available per- spectives. Specifically, G-PVR samples the feature at loca- tions of reference point p to construct keys and values for multi-head attention modules:
Liee = || Fpom Fincom ||,
(3)
3 = WWE, é= &W., with Ap- Ooffset VV, = o( (FPPvi + Ap),
(1)
where F piucom Decoder((V (Vcooee((Y.FPpv)) FPPv]). Then, we use PV-to-BEV feature transformation module to map the incomplete PV feature as Fincom into the BEV feature BEV
where is a learnable query. W& and Wg are transforma- tion matrices. i and i are the key and value embeddings. Ooffset is a light weight network to generate offsets. & is sampling function from deformable attention (Zhu al., 2021). Fpy = Fpyon {Frmmen) }o is the feature of all avail- able views. A naive solution is to generate vanilla reference points from a uniform grid, which, however, ignores the important fact that different views contribute unequally to the reconstruction of the missing view. Adjacent views typi- cally contain the most relevant information and should be given higher importance, while more distant views (e.g., the opposite of the missing perspective) may contain less information and should be weighted accordingly.
3.4. Distill-based BEV Correction Module
In addition to reconstructing the perspective features of partially missing views, we also need to establish global BEV feature learning space to further correct the extraction of BEV features from incomplete observations. Specifically, we leverage the complete panoramic BEV features BEV as the supervisory signal correct the BEV features of incomplete observations Fincom via MSE loss:
Lcor MSE BEV Fincom )
(4)
Motivated by this, the G-PVR module incorporates prior knowledge about view importance to dynamically focus on the most informative regions, illustrated in Fig. 3. Specif- ically, using the left and right views of the missing perspec- tive starting point, tiled all available frames, based on the spatial distance to the missing frame, to construct panoramic perspective view as Fppy = Concat (Fn E R NN xC where N₂ denotes the number of available views and concat is the concatenation operation. Then, a set of Gaussian-bart reference points is generated to cover
We use LCor as one of the correction objectives to enable the panoramic BEV features of incomplete observations can implicitly benefit from the panoramic BEV features of complete observations during the training phase.
3.5. Map Decoder
We follow (Liao et al., 2023a) and adopt its BEV feature decoder, composed of several decoding layers and predic- tion head. Each decoding layer learns through self-attention

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SafeMap: Robust HD Map Construction from Incomplete Observations
and cross-altention. Self-attention is utilized facilitate interaction between hierarchical map queries in decoupled manner. Cross-altention in the decoder is specifically de- signed to enable interaction between map queries and input BEV features. Specifically, the input to the BEV Decoder is the panoramic BEV features from incomplete observations Fineve. Then, the Map head (MapHead) employs the clas- sification and point branches to produce the final predictions of map element categories and point positions.
Table 1. Performance comparisons with (Liao al., 2023a) when
losing each of six camera views on the nuScenes validation set.
Standard
Method
AP ped.
AP div.
AP bou.
mAP
MapTR
46.3
51.5
53.1
50.3
All Views
Ours
48.1
54.3
55.3
52.5
View Missing
Method
AP post
AP....
AP
mAP
Front View (Center)
MapTR Ours
25.7
34.5
33.6
31.3
36.6
45.0
45.8
42.4
Front Left View (1.eft)
MRpTR
37.9
47.8
45.6
43.7
3.6. Overall Optimization Objective
Ours
44.3
52.0
52.4
49.5
To improve the accuracy of online construction of vectorized HD maps in the presence of incomplete observations, we integrate the map loss Lmap with the above reconstruction losses, including the Perspective View Reconstruction loss (CRee) and the BEV Feature Correction loss (Ccor). The overall training objective can be formulated as:
Front Right View (Right)
MRpTR
38.8
46.6
47.1
44.2
Ours
45.3
52.3
51.7
49.4
Back View (Center)
MapTR
33.4
25.2
27.0
28.5
Ours
39.6
40.8
41.2
40.5
Back Left View (Left)
MapIR
41.3
48.3
47.8
45.8
Ours
45.5
53.1
52.9
50.5
Back Right View
MRpTR
41.2
49.5
47.8
46.1
= Imap + LRee + dz CCo::
(5)
(Right)
Ours
46.4
53.1
53.2
50.9
where A and X2 are hyper-parameters for balancing these losses. Cmap is calculated following (Liao et al., 2023a), which is composed of three parts, L.e. classification loss (Mukhoti et al., 2020), point2point loss (Malkauthekar, 2013), and edge direction loss (Liao et al., 2023a).
the X/Y axes, respectively.
Implementation Details. Our proposed SafeMap frame- work is trained using four NVIDIA RTX 3090 GPUs. To simulate real scenarios, we randomly discard RGB images of any view in each sample during training, reflecting the loss of frames from that view in actual situations. We se- lect two baseline models, MapTR (Liao et al., 2023a) and HIMap (Zhou et al., 2024), and retrain them using their configurations. All experiments utilize the AdamW opti- mizer (Loshchilov & Hutter, 2019) with a learning rate of 4.2 x 10-A, fine-tuning for 8 epochs, while hyperparameters A and A2 are set to 0.05 and 5, respectively.
4. Experiments
4.1. Experimental Settings
Datasets. The nuScenes dataset (Caesar et al., 2020) contains 1,000 sequences of recordings collected by au- tonomous driving cars. Each sample is annotated at 2Hz and includes six camera images covering the 360 horizontal FOV of the ego-vchicle. For fair evaluation, we follow (Liao et al., 2023a; Zhou et al., 2024) and focus on three types of map elements: pedestrian crossings, lane dividers, and road boundaries. The Argoverse2 dataset (Wilson et al., 2021) consists of 1,000 logs, each capturing 15 seconds of 20Hz RGB images from 7 cameras, 10Hz LiDAR sweeps, and 3D vectorized map. The dataset is split into 700 logs for training. 150 logs for validation, and 150 logs for testing. Consistent with previous works (Liao et al., 2023a), we re- port results on the validation set and focus on the same three map categories as the nuScenes dataset.
The original images have a resolution of 1, 600 x 900, re- sized by a factor of 0.5 during training. We limit the maxi- mum number of map elements per frame to 100, with each containing 20 points. The size of each Bird's-Fiee-a View (BEV) grid is set to 0.75 meters, and the transformer de- coder is configured with two layers. For the G-PVR mod- ule, the SafeMap model undergoes fine-tuning for 8 epochs on the nuScenes dataset and 2 epochs on the Argoverse2 dataset, with batch sizes set to and 6, respectively. During inference, evaluate the model across possible scenarios.
Evaluation Metrics. We use metrics consistent with pre- vious HD map works (Liao al., 2023a; Li al., 2022a: Zhou et al., 2024). Average precision (AP) used assess map construction quality, while Chamfer distance (Dehamter) measures the alignment between predictions and ground truth. We calculate AP, under various DChamfer thresholds (T E T = 0.5m, 1.0m, 1.5m), averaging across all thresh- olds to obtain the final mean AP (mAP) metric. The percep- tion ranges are [-15 1-15.0m, 15.0m) and (-30.0m, 30.0m] for
4.2. Comparisons with State-of-the-Art Methods
Results on nuScenes. Tab. I and Tab. 2 present the re- sults of our SafeMap model compared to the representative MapTR (Liao et al., 2023a) and HIMap (Zhou et al., 2024) methods in the HD map construction task under both com- plete and incomplete observations on the nuScenes dataset. For fairness, we used the official model configurations pro- vided by the open-source codebases and retrained the mod-
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SafeMap: Robust HD Map Construction from Incomplete Observations
Table Performance comparisons with (Zhou al., 2024) when
Table 3. Performance comparison MapTR (Liao al., 2023a)
losing each of six camera views the nuScenes validation set.
when losing each of seven camera views on Argoverse2 val set.
Standard
Method
AP ped.
APdio.
APt Bow.
mAP
Standard
Method
AP pod.
AP div.
AP bou.
mAP
MIMap
62.2
66.5
67.9
MapTR
57.7
58.9
59.4
58.7
All Views
All Views
Ours
62.6
66.7
68.7
66.0
Ours
58.7
59.7
60.6
59.7
View Missing
Method
AP post
mAP
View Missing
Method
AP prst.
AP....
AP
mAP
Front View
HIMap
39.2
41.6
38.0
Front View
MapTR Ours
50.8
53.9
51.5
(Center)
Ours
50.7
55.7
56.3
54.3
(Center)
53.8
54.9
58.1
55.6
Front Left View
HIMap
51.5
59.6
60.0
57.0
Front Left View
MRpTR
51.8
55.4
53.4
53.5
(l.fft)
Ours
59.2
64.2
65.7
63.0
(1.eft)
Ours
54.6
58.6
57.9
57.0
Front Right View
HIMap
57.0
62.1
62.6
60.6
Front Right View
MRpTR
52.7
57.3
54.2
54.7
(Right)
Ours
60.0
64.2
66.1
63.4
(Right)
Ours
55.6
58.9 98.9
57.3
57.3
Back View
MIMap
46.3
31.4
21.7
33.1
Rear Left View
MapTR
50.5
48.1
50.0
49.5
(Center)
Ours
51.4
50.8
51.6
51.3
(Left)
Ours
54.6
53.5
55.5
54.7
Back Left View
HlMap
58.1
63.8
64.2
62.0
Rear Right View
MapTR
49.1
52.6
47.0
49.6
(Left)
Ours
60.7
65.4
67.0
64.4
(Right)
Ours
54.8
57.2 2
54.0
55.3
Back Right View (Right)
HIMap
58.5
62.8
63.4
61.6
Side Left View (Left)
MapTR
55.0
57.9
57.2
56.7
Ours
60.8
65.1
66.5
64.2
Ours
57.4
59.5
59.7
58.9
Side Right View
MapTR
56.0
58.3
57.8
57.4
(Right)
Ours
58.0
59.3
59.8
59.1
els according to their default settings. The experimental results reveal the following: 1) Both MapTR and HIMap models perform poorly when the 'CAM FRONT' and 'CAM BACK' views are missing. These views have the most sig- nificant impact on model performance due to their inclusion of more critical map elements. 2) The SafeMap model con- sistently outperforms the source MapTR/HIMas models in both complete and incomplete observation scenarios. No- tably, SafeMap improves the HIMap model's mAP metric by 2.4% to 18.2% across various missing camera view demonstrating its superior generalization capability in HD map construction. SafeMap can be seamlessly integrated into existing models, offering plug-and-pla solution for enhanced robustness. Its excellent performance under in- complete view conditions is attributed to its comprehensive feature reconstruction capability, which effectively restores missing view features from incomplete data.
Table 4. Ablation study of components on the Gaussian based Perspective View Reconstruction module (G-PVR) and the Distill- based BEV Correction module (D-BEVC).
G-PVR
D-BEVC
AP ped.
AP APaio. din.
AP bou.
mAP
x
X
36.4
42.0
41.5
39.9
/
x
42.7
47.7
49.2
46,5
x
/
42.4
47.7
49.4
46.5
cases,
42.9
49.4
49.5
47.3
generalization ability of our method across different sensor configurations in the HD map construction task. Overall, SafeMap significantly outperforms previous methods across both complete and incomplete observations, showcasing the benefits of the G-PVR and D-BEVC modules.
Results on Argoverse2. Tab. 3 compares the performance of our SafeMap model with the popular MapTTR(LLio al., 2023a) model on the Argoverse2 dataset. Compared to the nuScenes dataset, both SafeMap and MapTTR exhibit less performance degradation under incomplete view conditions. This is likely due to the Argoverse2 dataset capturing ob- servations from seven viewpoints, whereas the nuScenes dataset only has six. In the complete view observation setting, our SafeMap model achieves 1.0% absolute im- provement in mAP compared to the source MapTTR model. Under incomplete observations, SafeMap significantly out- performs the baseline TR model across various missing camera view scenarios. For instance, when the front camera view is missing, SafeMap achieves a 4.1% improvement in mAP over MapTR. These results further demonstrate the
4.3. Ablation Study
In this section, we analyze the SafeMap model to verify the effectiveness of the proposed method. Unless otherwise specified, all experiments use Map'IR as the baseline model on the nuScenes dataset. For brevity, we report average metrics across six missing camera view cases the ablation experiments.
Analysis Different Modules. To systematically evaluate the contribution of each module in SafeMap. we conducted ablation studies by removing components individually and presenting the results in Tab. 4. The following ablation models were designed: 1) SafeMap (Baseline): the model trained without the reconstruction module; 2) SafeMap (w/
6

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SafeMap: Robust HD Map Construction from Incomplete Observations
60
Table 5. Ablation study the use of the G-PVR module.
Setting
AP
P ped.
AP di..
AP ton.
mAP
55
SafeMap (w/o G-PVR)
42.4
47.7
49.4
46.5
SafeMap (w/ Mean-PVR)
36.8
41.0
42.8
39.9
50
SafeMap (w/ MAE-PVR)
42.3
47.9
49.2
46.5
mAp
SafeMap (w/ Standard-PVR)
42.6
48.9
48.7
46.8
SafcMap (w/ Gaussian-PVR)
42.9
49.4
49.5
47.3
45
G-PVR IC
Table 6. Ablation study the use of the distillation loss.
L-PVR
G-PVR C
Method
APped
APa dvo.
APbou.
mAP
L-PVR C
35
SafeMap (w/ D-BEVC)
42.7
47.7
49.2
46.5
ft-4
ft-8
ft-16
ft-24
SafeMap (w/ KL)
42.3
48.6
49.5
16.8
SafeMap (w/ L1)
42.8
49.2
49.4
47.1
Figure 4. Comparisons the Gaussian- based PVR vs. Local PVR.
SafeMap (w/ L2)
42.9
49.4
49.5
47.3
ft-N denotes fine-tuning N epochs.
calculates the mean of all available views, MAE-PVR uses all views to reconstruct the missing with masked token, and Standard-PVR utilizes deformable attention module to aggregate information from available views.
G-PVR): the model trained with the Raussian-based Per- spective View Reconstruction (G-PVR) module; 3) SafeMap (w/ D-BEVC): the model trained with the Distill-based BEV Correction (D-BEVC) module; 4) SafeMap with both G- PVR and D-BEVC modules (the default setting). As shown in Tab, 4, the results demonstrate that SafeMap models with the G-PVR and D-BEVC modules significantly outperform the baseline, verifying the effectiveness of these modules in reconstructing missing camera view features. The ablation study confirms that each module in SafeMap meaningfully contributes to improving performance in settings with in- complete observations.
As shown in Tab. 5, the experimental results reveal the following: 1) The Mean-PVP variant underperformed com- pared the baseline model, indicating that simple PVR methods are insufficient for reconstructing missing view in- formation. 2) The G-PVR module consistently outperforms both the MAE-PVR and Standard-PVR variants. This per- formance is attributed to G-PVR's ' use of cauussian-based sparse sampling to generate strategic reference points, which serve as spatial priors for missing views. By leveraging these reference points, deformable attention focuses on the most informative regions across all available views, facilitating efficient reconstruction of the missing data. These findings confirm that the G-PVR module effectively utilizes valuable information in the remaining complete views to reconstruct the missing view information.
Gaussian based PVR vs. Local PVR. To explore the im- pact of different Perspective View Reconstruction strategies, evaluated Gaussian-based and Local-level reconstruction modules and presented the results in Fig. 4. The strategies are as follows: 1) Gaussian-basee PVR: uses all available views as context to reconstruct missing view information by leveraging relationships among camera views. 2) Lo- cal PVR: follows the approach of M-BEV (Chen et al., 2024b), using context from neighboring views to recover missing views. As shown in Fig. 4, the Gaussian-based PVR method consistently outperforms the Local PVR method in all settings, including both complete and incomplete obser- vations. The superior performance of the Gaussian-be baase PVR module is attributed to its ability to leverage all views for reconstructing missing map elements, which is crucial for enabling the ego-vehicle to accurately locate itself and anticipate upcoming features.
A Ablation Study on Distillation Loss. We also investigated the impact of different distillation loss functions within the D-BEVC module. The evaluated loss functions in- clude Manhattan distance (I1). Euclidean distance (L2). and Kullback-Leibles Divergence (K L). As shown in Tab. 6, SafeMap utilizing the KL divergence demonstrated inferior performance compared to both T, and T.2- The experimen- tal results indicate that SafeMap with L2 achieves the best performance, which explains why L2 was selected as the default configuration in our experiments.
Ablation Study G-PVR Module. To systematically evaluate the effectiveness of the G-PVR module, we trained the model with various variants and reported the mAP re- sults in Tab. 5. The variants include RVRR, MAE-PVR, Standard- PVR, and G-PVR. Each variant employs a differ- ent approach reconstruct the missing view. Mean-PVR
Impact of Different Numbers of Missing Views. To in- vestigate the impact of varying numbers of missing camera views on HD map construction, we randomly zeroed out 1, 2, 3, 4, and 5 camera images in the nuScenes dataset. Each configuration yields several combinations of missing views; specifically, there are combinations for missing

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SafeMap: Robust HD Map Construction from Incomplete Observations
Table 7.
Impact of different numbers of missing views.
Table 9, Experimental results on the robustness of HD map con-
struction under camera sensor corruptions.
Method
#View
AP pei..
AP dév.
AP bou.
mAP
Method
AP
AP
AP
mAP
mRR1
mCE]
1x
36.4
42.0
41.5
39.9
MapTR
46.3
51.5
53.1
50.3
49.3
100.0
2x
27.5
31.3
29.9
29.6
SafeMap
48.1
54.3
55.3
52.5
51.2
90.6
MapTR
18.8
20.9
19.5
HIMap
62.2
66.5
67.9
65.5
56.6
100.0
4x
11.0
11.6
9.4
10.6
SafeMap
62.6
66.7
68.7
66.0
62.8
83.2
5x
4.6
4.3
3.0
4.0
1x
42.9
49.4
49.5
47.3
Sataltap/MagtR Sateltap01 Maps
1a SafetoopHMMool
2x
33.5
37.0
35.0
35.2
SafeMap
23.7
24.4
20.2
22.8
4x
15.6
16.2
12.6
14.8
5x
6.7
7.0
4.3
6.0
0.0 0.02 0.53
0.00
0.05
0.00 5.07
0.00
0.05
30
A1
A2
Table Accuracy-computatiie analysis.
We report the mAP performance under the "complete" "iincomplete" observations.
Figure 5. Sensitivity Analysis of Hyperparameters À and A2.
Method
mAP
GPU Mem
Param
FPS
MapTR
50.3 / 39.9
2298 MB
39.1 M 39.5 M
21.5
formance against eight real-world camera sensor corruptions from MapBench (Hao al., 2024a), categorized into exte- rior environments, interior sensors, and sensor failures. The details of these corruptions are outlined in MapBench (Hao et al., 2024a). As shown in Tab. 9, SafeMap achieves gains of 1.9% in mRR and 9.4% in mCE compared to the baseline
SafeMap
52.5 47.3
2300 MB
21.4
HIMMap
65.5 52.1
4091 MB
68.1 M
9.7
SafeMap
66.0 60.1
4155 MB
71.7 M
9.2
view, 15 for 2, 20 for 15 for and for We com- pute the average AP and mAP metrics for each combination to derive the final results. To demonstrate the robustness of our model, we trained it to handle all these cases. As shown in Tab. 7, the results reveal that as the number of missing views increases, model performance declines, as ex- pected. Compared the MapTR (Liao al., 2023a) model, SafeMap shows significantly less performance degradation with missing perspectives. This resilience stems from the ability of SafeMap to effectively reconstruct features from incomplete views, enhancing robustness despite multiple missing cameras.
MapTR model. It also shows improvements of 6.2% mRR and 16.8 16.8% in mCE over the original HIMap model. These results underscore SafeMap's effectiveness in enhancing the robustness of camera based HD map construction methods against various sensor corruptions.
48
47
mAP
46
Inference Speed, Model Size & GPU Memory. To eval- uate the effectiveness of SafeMap, we analyzed its perfor- mance in terms of accuracy, model size, GPU memory age, and inference speed. The results in Tab. 8 reveal sev- eral key findings: 1) SafeMap significantly outperforms the source MHDMR/HIMMp models in both complete and incom- plete observations. 2) In terms of model size, SafeMap only increases the number of parameters by 0.4MB to 3.6MB compared to the source models. 3) Regarding GPU memory and inference speed, SafeMap and the source models show nearly identical metrics. Overall, SafeMap achieves substan- tial performance improvements over the baseline models with minimal increases in parameters, while maintaining comparable inference speed and memory usage.
45
1
2
3
4
5
0
Figure Sensitivity Analysis Hyperparameter Variance d.
4.4. Sensitivity of Hyper-parameters
Sensitivity Analysis of Hyperparameters \ and Ag. We analyze the sensitivity of hyperparameters Àı and Ag, as illustrated in Fig. 5. The results presented focus the miss Front View setting for the SafeMap (MapTRR) and SafeMap (HIMap) methods on the nuScenes dataset. By varying X and X2 within a feasible range while keeping other hy- perparameters at their default values, we gain insights into each component's contribution to overall performance. As
Robustness against Camera Sensor Corruptions. To fur- ther evaluate the robustness of SafeMap, we assessed per-
8

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SafeMap: Robust HD Map Construction from Incomplete Observations
Incomplete observations
GT
MapTR
SafeMap
Incomplete observations
GT
MapTR
SafeMap
Figure 7. Qualitative Comparisons. The camera view marked with the symbol X indicates the absence of this perspective.
shown, progressively increasing A1 from 0.01 to 0.09 leads to consistent improvements, achieving optimal performance Y = 0.05. Additionally, Fig. 5 demonstrates that the best performance for our method occurs at λ2 = 5. Therefore, set A to 0.05 and X2 to 5 for all configurations.
5. Conclusion
In this paper, we present SafeMap, a novel robust frame- work for high-definition (HD) map construction that en- sures accuracy in the presence of missing camera views. At the core of SafeMap is the Gaussian-based Global View Reconstruction (G-GVR) module, which effectively lever- ages relationships among available camera views to infer missing information. Additionally, SafeMap incorporates Distillation-bas Bird's s-Eee-view Correction (D-BEVC) module, which uses the complete panoramic BEV features to further correct the BEV features extracted from incom- plete observations. Extensive experimental evaluations vali- date that SafeMap significantly outperforms baseline models across both complete and incomplete observation scenarios, achieving substantial performance gains while maintaining comparable inference speed and memory efficiency.
Sensitivity Analysis of Hyperparameter Variance a in Eq. 2. We conduct experiments using the results from the 'miss all view" setting for the SafeMap (MapTR) model on the nuScenes dataset. The sensitivity of hyperparameter variance a in Eq. 2 is illustrated in Fig. 6. Across a wide progressively increase the thresh-
range of in [1. 5]. old a in Eq. 2 from 1 to 5, our method consistently performs well, achieving the best results at a = 3. Therefore, we set to 3 for all configurations.
4.5. Qualitative Results
We present the online vectorized HD map construction re- sults for both the MapTR and Sap models under condi- tions of incomplete observations, as shown in Fig. 7. Our analysis reveals that the absence of different perspective images affects map construction to varying degrees. No- tably, the lack of front and back perspective images has significant negative impact, while the absence of other per- spectives exerts relatively minor influence. This finding is consistent with the results shown in Tab. 1 and Tab. 2, as front and back observations provide crucial information relevant to map elements. In comparison to the baseline MapTR model, SafeMap exhibits significant advantage in effectively modeling all map elements under conditions of incomplete observations. Overall, our model shows signifi- cant advantages in the incomplete observations setting.
Impact Statement
While SafeMap achieves impressive results in high- definition map construction with missing camera views, it does not address the challenges of multi-sensor fusion. This limitation could impact its effectiveness in real-world applications where data from multiple sensor modalities is critical. Future research should explore the development of multi-modal robust architectures that can effectively in- tegrate information from various sensor types, enhancing the overall resilience and accuracy of map construction in diverse environments. As far we know, our work does not have any negative ethical impacts concerning soci- etal consequences, as it focuses on advancing fundamental research in autonomous driving robustness.
Acknowledgment: This project has received funding from the NSF China (No. 62302139, No.62106259), and FRFCU China(No. PA2025IISL0115)

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SafeMap: Robust HD Map Construction from Incomplete Observations
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12


Reviews:
[{"review_id": "37oN2xG6s8", "reviewer": "Reviewer_smrB", "summary": "The paper introduces SafeMap, a novel framework designed to enhance the robustness of high-definition (HD) map construction for autonomous driving, particularly in scenarios where camera views are incomplete or missing. The key contributions of SafeMap are two innovative modules: the Gaussian-based Perspective View Reconstruction (G-PVR) and the Distillation-based Bird's-Eye-View Correction (D-BEVC). The G-PVR module leverages Gaussian-based sparse sampling to dynamically prioritize the most informative regions from available camera views, while the D-BEVC module uses complete panoramic BEV features to correct BEV representations derived from incomplete observations. The authors demonstrate that SafeMap significantly outperforms existing methods in both complete and incomplete observation scenarios, offering a plug-and-play solution that integrates seamlessly into existing systems. The paper also provides extensive experimental validation on the nuScenes and Argoverse2 datasets, showcasing the framework's superior performance and robustness.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "We sincerely thank the reviewer for the thoughtful and constructive feedback. We greatly appreciate your recognition of our \n*\"technical novelty\"*, *\"robustness in incomplete scenarios\"*, *\"experimental design\"*, and *\"plug-and-play applicability\"*. Below, we address your main questions and concerns in detail.\n\n---\n> ***`Q1`: \"Extending SafeMap to incorporate multi-sensor fusion.\"***\n\nA: Thank you for the insightful suggestion. To explore the effectiveness of SafeMap in multi-sensor fusion settings, we extended our framework to **MapTR-Fusion**, a recent **camera-LiDAR fusion** model, and evaluated it under **13 real-world multi-sensor corruptions** introduced in MapBench [1].\n\nThe results, presented in the following table, indicate that our SafeMap method not only enhances performance in scenarios involving camera view loss but also significantly boosts the efficacy of **multi-sensor fusion** methods.\n\n|**Method** |Cam. Miss | Cam. Crash  |Frame Lost | LiDAR Miss| Incom Echo | Cross Talk|Cross Sensor|Crash +Echo |Crash +Talk|Crash +Sensor|Frame +Echo| Frame +Talk|Frame +Sensor|Avg.|\n| :-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|MapTR|22.5|39.1|36.3|20.4|55.1|41.5|39.6|38.6|22.7|20.8|35.8|20.8|19.1|31.7\n|**SafeMap**|**27.6**|**42.1**|**39.8**|**23.6**|**58.3**|**46.2**|**42.1**|**41.3**|**26.7**|**22.7**|**37.2**|**25.3**|**22.7**|**35.0**\n| **$\\Delta$** |**+5.1**|**+3.0**|**+3.5**|**+3.2**|**+3.2**|**+4.7**|**+2.5**|**+2.7**|**+4.0**|**+1.9**|**+1.4**|**+4.5**|**+3.6**|**+3.3**\n\nThese results further strengthen the practical value of our approach in real-world autonomous driving systems. As suggested, we have added this discussion and table to the revised version.\n\n---\n> ***`Q2`: \"Could the authors discuss real-world deployment scenarios?\"***\n\nA: We appreciate your comment on real-world applicability. While large-scale real-world deployments are resource-intensive, we adopted MapBench [1] to systematically simulate **real-world conditions**. This benchmark includes eight **realistic camera sensor corruptions**, categorized into:\n1. Exterior environmental conditions (`Brightness`, `Low-Light`, `Fog`, and `Snow`).\n2. Interior sensor artifacts (`Motion Blur` and `Color Quantization`). \n3. Complete sensor failures (`Camera Crash` and `Frame Loss`). \n\n  As presented in **Table 9 of the main paper**, SafeMap consistently outperforms baselines under these settings:\n- `+1.9%` in mRR and `+9.4%` in mCE over **MapTR**.\n- `+6.2%` in mRR and `+16.8%` in mCE over **HIMap**.\n\nThese results clearly demonstrate our robustness under practical corruptions and affirm its suitability for real-world deployment. As suggested, we have included a more explicit discussion in the revised manuscript.\n\n---\n> ***`Q3`: \"More detailed explanations of Gaussian-based sparse sampling.\"***\n\nA: Thanks for your comments. Our **G-PVR module** employs Gaussian-based sparse sampling to guide reference point selection in the reconstruction of missing camera views. The intuition is to **prioritize sampling locations near adjacent views**, which typically contain the most informative cues for reconstructing the missing view.\n\nConcretely, the horizontal coordinate of reference points is sampled as:\n\n$p_x ∼ 𝓝(μ, σ²),$\n\nand the vertical coordinate is sampled as:\n\n$p_y ∼ 𝕌(0, H),$\n\nwhere:\n- $μ$ is set to the center of the panoramic perspective view $(μ = (N_a × W) / 2)$.\n- $σ²$ is a hyperparameter controlling the horizontal spread (tuned via ablation in Appendix C).\n- $H$ is the vertical resolution of the input image.\n\nThese coordinates define a 2D sampling grid, where reference points are more densely sampled around the central horizontal region (typically where adjacent views reside), and uniformly along the vertical axis.\n\nThis sampling pattern is utilized in the Deformable Attention module [2]. This mechanism allows the model to dynamically focus on the most informative regions, concentrating attention near adjacent views while still capturing a broader context. As shown in **Table 5 and Figure 4 in the main paper**, this targeted sampling strategy leads to improved reconstruction quality compared to uniform or local sampling strategies.\n\n---\n**Reference:**\n\n[1] Hao et al. Is Your HD Map Constructor Reliable under Sensor Corruptions? NeurIPS, 2024.\n\n[2] Zhu et al. Deformable DETR: Deformable Transformers for End-to-End Object Detection, ICLR 2021.", "claims_and_evidence": "The claims in the submission are largely supported by clear and convincing evidence. The authors provide extensive quantitative results on benchmark datasets (such as nuScenes and Argoverse2) that demonstrate significant improvements in metrics like mAP under both complete and incomplete observation scenarios. In addition, thorough ablation studies isolate the contributions of the Gaussian-based Perspective View Reconstruction (G-PVR) and Distillation-based BEV Correction (D-BEVC) modules, reinforcing their effectiveness in reconstructing missing views and correcting BEV features. Results comparing with state-of-the-art methods, as well as experiments on robustness against camera sensor corruptions (e.g., using MapBench), further substantiate their claims.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are well-suited to address the challenges of robust HD map construction for autonomous driving. The approach integrates a Gaussian-based Perspective View Reconstruction (G-PVR) module and a Distillation-based BEV Correction (D-BEVC) module to effectively restore missing view information and correct BEV features derived from incomplete observations, a critical need given the real-world occurrence of sensor failures and occlusions. Moreover, the evaluation criteria—including average precision under various Chamfer distance thresholds, along with experimental validation on established benchmark datasets like nuScenes and Argoverse2—provide a comprehensive measure of both spatial accuracy and overall system robustness. Together, these methods and evaluation metrics offer a balanced and rigorous framework that addresses the core challenges of ensuring accurate and reliable map construction in complex autonomous driving scenarios.", "theoretical_claims": "This is an application paper.", "experimental_designs_or_analyses": "Please refer to Claims And Evidence part.", "relation_to_broader_scientific_literature": "The paper builds on existing multi-view camera-based HD map construction methods, which have gained attention due to advancements in Bird's-Eye-View (BEV) perception (e.g., MapTR, HIMap). However, it addresses a critical limitation of these methods—their reliance on complete multi-view images—by introducing the G-PVR and D-BEVC modules. The G-PVR module leverages Gaussian-based sparse sampling, a novel approach that dynamically prioritizes informative regions from available views, which is a significant departure from traditional methods that rely on uniform sampling or cropping adjacent views. The D-BEVC module, which uses complete panoramic BEV features to correct incomplete observations, is also a novel contribution that enhances robustness. These innovations are particularly relevant given recent works like MetaBEV and UniBEV, which have started to tackle sensor failures but still rely on complete multi-view images. By addressing the challenge of incomplete observations, SafeMap fills a gap in the literature and provides a robust solution that aligns with the broader trend of enhancing the reliability of autonomous driving systems.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "## Paper Strengths\nThe introduction of the G-PVR and D-BEVC modules is a significant technical advancement. The G-PVR module's use of Gaussian-based sparse sampling to reconstruct missing views is particularly innovative, as it dynamically focuses on the most informative regions across available views. The D-BEVC module's use of complete panoramic BEV features to correct incomplete observations is also a novel approach that enhances the robustness of HD map construction.\n\nThe paper addresses a critical challenge in autonomous driving—handling incomplete or missing camera views—which is a common issue in real-world applications. The proposed framework demonstrates strong performance even when crucial camera views are missing, making it highly applicable to practical autonomous driving systems.\n\nThe authors provide extensive experimental validation on two widely used datasets (nuScenes and Argoverse2). The results show significant improvements over state-of-the-art methods, particularly in scenarios with missing camera views. The ablation studies further validate the effectiveness of each module, providing strong evidence for the framework's robustness.\n\nSafeMap is designed to be easily integrated into existing systems, which is a practical advantage for real-world deployment. The framework does not require significant changes to existing pipelines, making it a highly attractive solution for enhancing the robustness of HD map construction.\n\n## Major Weaknesses\nWhile the paper focuses on camera-based HD map construction, it does not address the integration of other sensor modalities (e.g., LiDAR, radar) that are commonly used in autonomous driving systems. This limitation could impact the framework's effectiveness in real-world scenarios where multi-sensor fusion is critical for robust perception.\n\nAlthough the framework is validated on standard datasets, there is no discussion or validation of its performance in real-world deployment scenarios. Real-world conditions, such as dynamic environments, varying lighting conditions, and sensor noise, could pose additional challenges that are not captured in the current experiments.", "other_comments_or_suggestions": "NA", "questions_for_authors": "1. Could the authors discuss the potential for extending SafeMap to incorporate multi-sensor fusion, particularly LiDAR and radar? How might this integration affect the framework's robustness and performance in real-world scenarios?\n2. The G-PVR module uses Gaussian-based sparse sampling. Could the authors provide more detailed explanations or visualizations of this process?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "51fOArq0q0", "reviewer": "Reviewer_8wHx", "summary": "This paper presents SafeMap, an HD map prediction model for autonomous driving scenarios. Its uniqueness lies in its focus on robustness, particularly in handling incomplete multi-camera input views, which may occur in real-world scenarios due to sensor failure or occlusions. The framework integrates two key modules, Gaussian-based Perspective View Reconstruction (G-PVR) and Distillation-based BEV Correction (D-BEVC), to reconstruct missing view information and correct the Bird’s-Eye-View (BEV) features, respectively. Extensive evaluations demonstrate improved performance over baseline models.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "We sincerely thank the reviewer for the positive evaluation and thoughtful comments. We appreciate your recognition that our work *\"provides new insights into fault-tolerant HD map reconstruction for autonomous driving\"*. Below, we address your specific concerns in detail and will incorporate corresponding improvements into the revised manuscript.\n\n---\n> ***`Q1`: \"More experiments on random missing views and real-world camera sensor corruptions are needed.\"***\n\nA: Thanks for your suggestion. We have conducted extensive robustness evaluations addressing both random view drops and real-world sensor corruptions:\n- **Random Missing Views:** \n  - As detailed in **Table 7 of the main paper**, we simulated **1 to 5 randomly missing camera views** using the 6-camera setup in nuScenes. Each missing-view setting involves **multiple combinations** (e.g., 6 for 1 missing view, 15 for 2 missing views, 20 for 3, 15 for 4, and 6 for 5), and SafeMap is trained to generalize across all these cases, maintaining high performance with minimal degradation—credited to its ability to infer missing cues via G-PVR and D-BEVC.\n\n- **Real-World Sensor Corruptions:**\n  - We adopt MapBench [1], which includes eight realistic corruptions across:\n    1. Environmental conditions: `Brightness`, `Low-Light`, `Fog`, and `Snow`;\n    2. Sensor artifacts: `Motion Blur` and `Color Quantization`;\n    3. Sensor failures: `Camera Crash` and `Frame Loss`.\n  - As shown in **Table 9 of the main paper**, SafeMap outperforms MapTR by `+1.9%` mRR / `+9.4%` mCE and HIMap by `+6.2%` mRR / `+16.8%` mCE, demonstrating robustness beyond simple view dropouts and toward real-world deployment scenarios.\n\n---\n> ***`Q2`: \"In Eq (2), why do pixel `px` and pixel `py` follow different noise distributions?\"***\n\nA: Thank you for this insightful question. Our design choice is based on empirical observation and domain priors in autonomous driving:\n- The **horizontal coordinate** `px` follows a **Gaussian distribution** centered on the middle of the panoramic perspective view. This reflects the fact that **adjacent views to the missing one contain the most informative features for reconstruction**. The Gaussian distribution allows us to focus attention on these nearby views while still softly exploring farther regions.\n- The **vertical coordinate** `py` follows a **uniform distribution**. This is because **vertical information** (e.g., lane boundaries, poles, road textures) is uniformly important across different heights in the image, especially in BEV-centric tasks like HD map construction.\n\nThis **hybrid sampling strategy** balances spatial focus and coverage—allowing G-PVR to direct more attention toward informative horizontal regions while still preserving vertical completeness. Appendix C provides a **sensitivity analysis** showing that this setup offers superior performance over uniform sampling in both dimensions.\n\n---\n> ***`Q3`: \"The backbone builds on MapTR. What is the motivation of the architecture?\"***\n\nA: We appreciate this concern. While SafeMap builds upon MapTR for its base encoder-decoder structure, our contributions lie in robust map construction under incomplete observations, which MapTR and existing works are not designed to address. Specifically, our architectural novelty includes:\n- The **G-PVR module**, which performs dynamic reference point sampling using Gaussian priors and deformable attention to reconstruct missing views;\n- The **D-BEVC module**, which distills global spatial priors from complete panoramic views and corrects BEV representations derived from incomplete inputs.\n\nThese modules are **model-agnostic** and can be **plugged into other backbones** (e.g., StreamMap), offering a general solution for improving robustness. As shown in the experiments below, SafeMap consistently outperforms both **MapTR** and **StreamMap** variants when camera views are missing:\n\n- *Table. Performance on individual missing views (nuScenes, MapTR backbone):*\n|**View Missing**|**Front**|**Front-L**|**Front-R**|**Back**| **Back-L**|**Back-R**\n| :-|:-:|:-:|:-:|:-:|:-:|:-:|\n|MapTRv2|40.3|53.7|54.2|41.2|56.3|57.4|50.5|\n|**SafeMap**|**53.6**|**59.7**|**59.1**|**52.3**|**60.1**|**60.8**\n| **$\\Delta$** |**+12.3**|**+6.0**|**+4.9**|**+11.1**|**+3.8**|**+3.4**\n\n- *Table. Extension to StreamMap (temporal BEV backbone):*\n|**View Missing** | **Front** | **Front-L**  |**Front-R**|**Back**| **Back-L** | **Back-R**|\n| :-|:-:|:-:|:-:|:-:|:-:|:-:|\n|StreamMap (w/o temp)|13.2|17.4|18.0|14.1|16.3|17.1\n|StreamMap (w/ temp)|14.3|18.2|19.3|15.3|17.5|18.5\n|**SafeMap (w/ temp)**|**18.7**|**21.5**|**21.2**|**19.3**|**19.8**|**22.3**\n\nThese improvements verify that SafeMap is not merely an application of MapTR, but rather a **general framework** that enhances robustness and reliability under **real-world** constraints, a critical capability not addressed by prior methods.\n\n---\n**Reference:**\n\n[1] Hao et al. Is Your HD Map Constructor Reliable under Sensor Corruptions? NeurIPS, 2024.", "claims_and_evidence": "The paper is well written and easy to follow. One concern is that while the method claims robustness in real-world scenarios, the failure case considered is a bit too simple (just missing one input image). I would like to see more ablations in this regard—for example, scenarios with n random missing views and cases where random patches or blocks in an input image are missing, rather than completely losing an entire view.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "In Eq(2), why do pixel px and pixel py follow different noise distributions?", "experimental_designs_or_analyses": "Checked.", "relation_to_broader_scientific_literature": "This paper will provide new insights into fault-tolerant systems for HD map reconstruction in autonomous driving.", "essential_references_not_discussed": "None.", "other_strengths_and_weaknesses": "Because the method's backbone heavily relies on MapTR, the model architecture lacks significant novelty.", "other_comments_or_suggestions": "None.", "questions_for_authors": "Please address the questions and concerns mentioned above.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AkXg5RJsRz", "reviewer": "Reviewer_kzrj", "summary": "This paper presents a method that tackles the challenge of learning-based multi-view HD map reconstruction when one or more input views are missing. The proposed approach uses two new modules, G-PVR and D-bEVC, to aggregate information from available views and predict or correct the missing features, enhancing robustness in the presence of missing views. Experimental results demonstrate improved performance compared to the baseline methods, MapTR and HIMap, when views are missing.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "We sincerely thank the reviewer for the detailed and constructive feedback. We are encouraged that you found *\"the experimental results convincing\"* and *\"the supplementary material informative\"*. Below, we address your insightful concerns point by point and will incorporate the clarifications into the revised version of the manuscript.\n\n---\n> ***`Q1`: \"How does the method compare to other sparse-view HD map reconstruction approaches?***\n\nA: Thank you for this important question. Most existing HD map reconstruction methods—including **MapTR** and **HIMap**—assume a **full multi-view setup** (6 views in nuScenes, 7 in Argoverse2), and do not explore sparse-view conditions. To evaluate SafeMap under such settings, we simulated **1–5 randomly missing views** (out of 6) and compared against MapTR.\n\n**As presented in Table 7 of the main paper**, SafeMap demonstrates three key advantages:\n1. Only `2.3` mAP drop with **3 missing views**, vs. `8.1` mAP drop for MapTR.\n2. Maintains robust performance even with **4 missing views** (66% sparsity).\n3. Under extreme cases, i.e., **5 missing views**, SafeMap outperforms MapTR by `50%` in mAP.\n\nThese results confirm our generalizability to **sparse inputs**, supporting its practicality in **real-world applications** with limited or degraded sensor coverage.\n\n---\n> ***`Q2`: \"More experiment results on real-world camera sensor corruptions.\"***\n\nA:  To comprehensively evaluate real-world robustness, we adopt MapBench [1], which includes eight realistic corruptions:\n1. Environmental conditions: `Brightness`, `Low-Light`, `Fog`, and `Snow`;\n2. Sensor artifacts: `Motion Blur` and `Color Quantization`;\n3. Sensor failures: `Camera Crash` and `Frame Loss`.\n\nAs shown in **Table 9 in the main paper**, SafeMap outperforms **MapTR** by `+1.9%` mRR / `+9.4%` mCE and **HIMap** by `+6.2%` mRR / `+16.8%` mCE, confirming that our approach is robust not only to missing views but also to partial occlusion, adverse lighting, and noisy signals.\n\n---\n> ***`Q3`: \"How does SafeMap compare to data augmentation methods that simulate missing/corrupted views?\"***\n\nA: Thanks for your question. We conducted controlled experiments with two baselines:\n- **Random Mask Patch:** randomly masks image patches;\n- **Random Mask View:** randomly drops one full view.\n\nAs shown below, SafeMap outperforms both:\n|Method|AP. ped|AP. div|AP. bou|mAP|\n|-|-|-|-|-|\n|Baseline (MapTR)|36.6|42.0|41.5|39.9|\n|Random Mask Patch| 36.7|42.1|41.7|40.2|\n|Random Mask View| 39.1|43.5|44.2|42.3|\n|**SafeMap**|**42.9**|**49.4**| **49.5**|**47.3**|\n\nWhile masking-based augmentation improves robustness, it lacks explicit mechanisms to reconstruct missing features. In contrast, SafeMap:\n- **Explicitly learns spatial relationships** through the Gaussian-based PVR module;\n- **Leverages complete panoramic BEV priors** via the D-BEVC module.\n\nThis targeted feature recovery provides significantly more effective supervision, especially under severe view dropouts. As suggested, we have added this study in the revised version.\n\n---\n> ***`Q4`: \"Why define Gaussian in image space (pixels) instead of 3D space using camera parameters?\"***\n\nA: Thank you for raising this point. While it is true that pixel space is not linearly aligned with 3D space, we adopt a **Gaussian in image space** for **two practical reasons**:\n- **Efficiency:** Deformable attention operates in the image plane; defining Gaussian sampling here avoids computational overhead and enables seamless integration with pixel-based encoders.\n- **Effectiveness:** Our Gaussian prior focuses on adjacent views, which in multi-camera setups are spatially proximate. Despite 3D nonlinearity, the horizontal image plane is a strong proxy for angular continuity.\n\nMoreover, our **sensitivity analysis** (Appendix C) empirically confirms that Gaussian sampling in image space **improves reconstruction accuracy**. We agree that incorporating 3D-aware priors is an exciting direction and plan to explore this in future work.\n\n---\n> ***`Q5`: \"Why use a separate module to predict missing views rather than directly predicting from available ones?\"***\n\nA: Great question. SafeMap separates the process into **two stages**:\n- **G-PVR reconstructs missing views**, guided by spatial priors;\n- **D-BEVC corrects BEV features** using distilled panoramic features.\n\nThis two-stage design offers a clear advantage over end-to-end prediction from incomplete inputs. As shown in **Table 7 of the main paper**:\n- With 3 missing views: SafeMap achieves `22.8` mAP vs. `19.5` mAP with direct prediction;\n- With 5 missing views: SafeMap achieves `6.0` mAP vs. `4.0` mAP (a 50% relative improvement).\n\nThese gains highlight that explicit view completion and correction better preserve geometric consistency, especially under extreme sparsity. As suggested, we have made this discussion more explicit in the revised version.\n\n---\n**Reference:**\n\n[1] Hao et al. Is Your HD Map Constructor Reliable under Sensor Corruptions? NeurIPS, 2024.", "claims_and_evidence": "The improvements and performance of the system are well demonstrated under conditions where one or more views are missing. The ablation study demonstrates the effectiveness of the two proposed modules.", "methods_and_evaluation_criteria": "Only two methods, MapTR and HIMap, are compared. What about other methods that address missing information, particularly those focusing on building HD maps from sparse views, such as using only 4 views? How does the performance of the proposed method compare to these approaches?\n\nIt would be useful to evaluate the performance under more realistic corrupted scenarios, rather than just complete view missing. For example, how does the system perform in cases of partial occlusion, varying light conditions, noise, etc.?", "theoretical_claims": "The method is straightforward and effective, as demonstrated by the experiments. However, there is a significant concern regarding the details. The high-level approach of training the network to recover missing information involves randomly masking features and training the network to recover these missing features from other images. While it's beneficial to explicitly address the missing features, how does this approach compare to training MapTR with additional data augmentation, where some input images are also randomly corrupted? What makes the proposed method more effective in this context?\n\n\nThe construction of the panoramic perspective view seems straightforward. However, it is somewhat unusual that the Gaussian is defined in the image plane using pixel distance. In a real 3D environment, the distance would depend on the camera intrinsics and extrinsics. This means that the distance in image space may not be linear in 3D space, which raises concerns about the accuracy of the approach in representing real-world distances.\n\n\nThe author claims to be the first to address HD map reconstruction under incomplete multi-view camera data. The theoretical claims and the design of the new modules make sense and are well supported by the detailed descriptions in the supplementary materials.", "experimental_designs_or_analyses": "The experiments compared to the baseline make sense. However, the comparison is limited to just two baselines and lacks a broader comparison to other methods that aim to reconstruct HD maps from sparse views.", "relation_to_broader_scientific_literature": "This issue of incomplete views could also be beneficial for other multi-view estimation methods, such as human pose estimation, 3D object reconstruction, and more.", "essential_references_not_discussed": "It is unclear how the method compares to other approaches that address the reconstruction of HD maps from fewer views.", "other_strengths_and_weaknesses": "The reasonableness of the main design is unclear. Specifically, why is it considered beneficial to train an additional module to collect information from other views and predict the missing view, rather than directly predicting the final outputs from all the available views?", "other_comments_or_suggestions": "N/A", "questions_for_authors": "How does the proposed method compare to other methods that address missing information, particularly those focusing on building HD maps from sparse views (e.g., using only <5 views)? e.g., compare to training MapTR with additional data augmentation, where some input images are randomly corrupted.\n\nHow does the system perform in more realistic corrupted scenarios, such as partial occlusion, varying light conditions, noise, etc., rather than just complete view missing?\n\nWhy is the Gaussian defined in the image plane using pixel distance, however in 3D space, the distance depends on camera parameters, which may not be linear?\n\nWhy is it better to train an extra module to predict the missing view, instead of directly predicting the final output from all available views?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ox92dHLSPg", "reviewer": "Reviewer_bD2C", "summary": "This paper proposed SafeMap, which is designed to address missing camera views in HD map construction. A Gaussian-based perspective view reconstruction module and a distillation-based panoramic BEV feature correction module are presented. Experiments on nuScenes and  Argoverse2 datasets demonstrate the effectiveness of the proposed method.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "We sincerely thank the reviewer for your thoughtful and detailed feedback. We appreciate your recognition of our *\"design\"*, *\"formulation\"*, *\"experimental rigor\"*, and *\"supplementary material\"*. Below, we address each of your comments and suggestions in detail.\n\n---\n> ***`Q1`: \"Investigate which views are most critical for HD map construction.\"***\n\nA: Thank you for this insightful suggestion. We conducted a detailed analysis of individual camera importance by systematically removing each view and observing performance drops (mAP) across **MapTR** and **HIMap** on nuScenes, and **MapTR** on Argoverse2. The results reveal a clear hierarchy:\n- The **front-center view** is most critical (e.g., MapTR: `50.3` → `31.3` mAP; HIMap: `65.5` → `38.0`);\n- **Back views** show moderate importance (e.g., MapTR: `28.5` mAP when back is missing);\n- **Front-side views** have smaller but non-negligible impact;\n- **Side views** are least critical (e.g., Argoverse2: `58.7` → `56.7` mAP).\n\nThese findings highlight the need for greater robustness to front/back view losses and suggest differentiated optimization strategies for peripheral cameras. We have integrated this analysis into the revised version.\n\n---\n> ***`Q2`: \"Additional experimental results for MapTRv2.\"***\n\nA: Thanks for your comment. We conducted experiments comparing **SafeMap** to **MapTRv2** under various single-view-missing conditions on nuScenes. As shown below, SafeMap consistently improves robustness **across all view types**, demonstrating strong generalization and reliability in incomplete observation settings:\n|**View Missing** |**Front**| **Front-L**|**Front-R**|**Back**|**Back-L**|**Back-R**|**Average**|\n|-|:-:|:-:|:-:|:-:|:-:|:-:|:-:\n|MapTRv2|40.3|53.7|54.2|41.2|56.3|57.4|50.5\n|**SafeMap**|**53.6**|**59.7**|**59.1**|**52.3**|**60.1**|**60.8**|**57.6**\n| **$\\Delta$** |+12.3|+6.0|+4.9|+11.1|+3.8|+3.4|+7.1\n\nWe have included these findings in the revised manuscript to further reinforce our effectiveness.\n\n---\n> ***`Q3`: \"Impact of temporal information (e.g., StreamMapNet, DTCLMapper) on view missing.\"***\n\nA: Thanks for your suggestion. We conducted experiments using **StreamMapNet** to evaluate the role of **temporal cues** in mitigating view-missing issues. Results on single-view-missing settings (nuScenes) are shown below:\n|**View Missing**|**Front**|**Front-L**|**Front-R**|**Back**|**Back-L**|**Back-R**|**Average**\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:\n|StreamMap (w/o temp)|13.2|17.4|18.0|14.1|16.3|17.1|16.0\n|StreamMap (w/ temp)|14.3|18.2|19.3|15.3|17.5|18.5|17.2\n|**SafeMap (w/ temp)**|**18.7**|**21.5**|**21.2**|**19.3**|**19.8**|**22.3**|**20.5**\n\nWhile temporal fusion provides moderate gains, it does not fully address view-missing challenges. In contrast, SafeMap explicitly reconstructs missing views and corrects BEV features, leading to significantly stronger performance. We have included these experiments and analyses in the revised revision.\n\n---\n> ***`Q4`: \"Can SafeMap improve robustness under other corruption scenarios (e.g., MapBench)?\"***\n\nA: Thanks for your question. Yes. We evaluated SafeMap using the MapBench benchmark [1], which includes 8 real-world camera corruptions covering environmental conditions (e.g., `Fog`, `Snow`), sensor artifacts (e.g., `Motion Blur`), and total sensor failures. As shown in **Table 9 in the main paper**, SafeMap achieves:\n- `+1.9%` mRR / `+9.4%` mCE over **MapTR**;\n- `+6.2%` mRR / `+16.8%` mCE over **HIMap**.\n\nThese results confirm our robustness beyond view-missing scenarios, handling diverse sensor degradations encountered in **real-world** deployments.\n\n---\n> ***`Q5`: \"What are the training and inference costs of SafeMap?\"***\n\nA: Thanks for asking. SafeMap is **efficient** in both **training** and **inference**:\n- **Training:** `~32.5` minutes/epoch on 8× RTX 3090 GPUs.\n- **Inference (Table 8):** Compared to MapTR, SafeMap runs at `21.4` FPS vs. `21.5` FPS, with negligible differences in memory (`2300` MB vs. `2298` MB) and model size (`39.5`M vs. `39.1`M parameters).\n\nThese results demonstrate that SafeMap introduces **minimal computational overhead** while delivering substantial robustness and accuracy gains.\n\n---\n> ***`Q6`: \"Related Work — Comparison with POP-Net.\"***\n\nA: Thank you for highlighting this relevant work. We have included a discussion comparing SafeMap to POP-Net in the revised manuscript. Our key distinctions are:\n1. **Flexible Gaussian-based sampling** vs. rigid panoramic encoders;\n2. **BEV feature distillation** vs. token decomposition and aggregation;\n3. **Efficiency:** SafeMap avoids the added latency and complexity of POP-Net's dual-encoder design.\n\nThese distinctions reflect our emphasis on modularity, scalability, and robustness. We appreciate your suggestion and have clearly positioned our contributions relative to POP-Net in the Related Work section.\n\n---\n**Reference:**\n\n[1] Hao et al. Is Your HD Map Constructor Reliable under Sensor Corruptions? NeurIPS, 2024.", "claims_and_evidence": "The proposed method can improve the robustness when some camera views are missing. The ablation studies verify the effectiveness of the proposed modules. Experiments on two public benchmarks and studies on different numbers of missing views validate the consistent gains of the proposed solution.", "methods_and_evaluation_criteria": "Yes. The used nuScenes (6 cameras) and Argoverse2 (7 cameras) datasets provide comprehensive benchmarks for evaluating the proposed solution.", "theoretical_claims": "Yes. The designed modules are well formulated.", "experimental_designs_or_analyses": "Yes. The results in different incomplete observation settings are well analyzed.", "relation_to_broader_scientific_literature": "The proposed method is relevant to autonomous driving, which has the potential to enhance mapping reliability and driving safety.", "essential_references_not_discussed": "The recent work \"Camera-Based Online Vectorized HD Map Construction With Incomplete Observation\" also addressed map construction under incomplete observation, which should be discussed.", "other_strengths_and_weaknesses": "- In the experiments, in addition to studying the effects of missing different numbers of views, it would also be nice to investigate which views are the most important for HD map construction.\n- On each benchmark, only a single baseline is selected. It would be nice to use different baselines for an experiment to validate the consistent gains of your solution.\n- How about recent mapping methods like StreamMapNet and DTCLMapper which leverage temporal information? Would they be beneficial for addressing the view missing challenges to some extent by incorporating temporal cues? This should be discussed.\n- In addition to the missing views, would your method be beneficial for enhancing mapping robustness in other corruption scenarios like other corruptions in the MapBench? This should be assessed.", "other_comments_or_suggestions": "It would be nice to present some results of the training costs of the proposed method.", "questions_for_authors": "In addition to MapTR, would you consider MapTRv2 for an experiment to validate the consistent gains of your presented solution?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The ratings for this paper were all weak accept. All reviewers acknowledged the paper’s novelty and effectiveness in HD map reconstruction under incomplete camera observations and its practical relevance for autonomous driving. The major concerns are about the insufficient experiments on more complex scenarios, such as real-world data (all reviewers) and incorporate with different sensors (Reviewer smrB). Additionally, reviewers raised some technical questions regarding specific details, to which the authors provided satisfactory responses in their rebuttal with further clarifications. All reviewers subsequently agreed to maintain their weak accept recommendations. Based on this assessment, the AC recommends acceptance.
Decision: Accept (poster)