AsyncBEV: Cross-modal flow alignment in Asynchronous 3D Object Detection
Authors: Shiming Wang
Holger Caesar
Liangliang Nan
Julian F. P. Kooij
Venue: ICLR 2026 Poster (2026)
Keywords: multi-modal 3D object detection
autonomous driving
a synchronous fusion.
Primary area: applications to robotics, autonomy, planning

Abstract: In autonomous driving, multi-modal perception tasks like 3D object detection typically rely on well-synchronized sensors, both at training and inference. However, despite the use of hardware- or software-based synchronization algorithms, perfect synchrony is rarely guaranteed: Sensors may operate at different frequencies, and real-world factors such as network latency, hardware failures, or processing bottlenecks often introduce time offsets between sensors.  Such asynchrony degrades perception performance, especially for dynamic objects. To address this challenge, we propose AsyncBEV, a trainable, lightweight, and generic module to improve the robustness of 3D Bird's Eye View (BEV) object detection models against sensor asynchrony. Inspired by scene flow estimation,  AsyncBEV first estimates the 2D flow from the BEV features of two different sensor modalities, taking into account the known time offset between these sensor measurements.  The predicted feature flow is then used to warp and spatially align the feature maps, which we show can easily be integrated into different current BEV detector architectures (e.g., BEV grid-based and token-based).  Extensive experiments demonstrate AsyncBEV improves robustness against both small and large asynchrony between LiDAR or camera sensors in both the token-based CMT and grid-based UniBEV, especially for dynamic objects. We significantly outperform the ego motion compensated CMT and UniBEV baselines, notably by $16.6$ % and $11.9$ % NDS on dynamic objects in the worst-case scenario of a $0.5 s$ time offset. Code is available at \url{https://github.com/tudelft-iv/AsyncBEV}.

Published as a conference paper at ICLR 2026
ASYNCBEV: CROSS-MODAL FLOW ALIGNMENT IN ASYNCHRONOUS 3D OBJECT DETECTION
Shiming Wang, Holger Caesar, Liangliang Nan, Julian F.P. Kooij
Delft University of Technology {s wang-15, h. caesar, liangl lang.nnn.nann.. j.f.p. j @tuuuel ft.nl
ABSTRACT
In autonomous driving, multi-modes perception tasks like 3D object detection typ- ically rely on well-synehronized sensors, both at training and inference. However, despite the use of hardware- or software-based synchronization algorithms, per- feet synchrony is rarely guaranteed: Sensors may operate different frequencies, and real-world factors such as network latency, hardware failures, processing bottlenecks often introduce time offsets between sensors. Such asynchrony de- grades perception performance. especially for dynamic objects. To address this challenge, we propose AsyncBEV, a trainable, lightweight, and generic module improve the robustness of 3D Bird's Eye View (BEV) object detection models against sensor asynchrony. Inspired by scene flow estimation, AsyncBEV predicts dense 2D flow field directly from asynchronous multi-modal BEV features tak- ing into account the known time offset between these sensor measurements. The predicted feature flow is then used warp and spatially align the feature maps. which we show can easily be integrated into different current BEV detector archi- tectures (e.g., BEV grid-based and coken-based). Extensive experiments demon- strate AsyncBEV improves robustness against both small and large asynchrony between LiDAR or camera sensors both the token-based CMT and grid-based UniBEV especially for dynamic objects. We significantly outperform the ego motion compensated CMT and UniBEV baselines, notably by 16.6% and 11.9% NDS dynamic objects in the worst-case scenario of 0.5 time offset. Code is available /tttps://githbbs wtt.c/otttttteeeeeeee 1v -ww.ffttii/AAAeeeeee AsyncBEV
Concept of Asynchronous 3D Ooject Detection
Asynchronous Detection pretraiineed
c) Asymchronous Detection crCHM-AAsycDEE (ouns)
At
Figure 1: Multi-modal 3D object detection under asynchrony. shows general cept of asynchronous 3D object detection. b) shows multi-modal 3D object detection under sensor asynchrony of pretrained CMT (Yan al. (202).. Predictions exhibit large translation errors. c) shows our AsyncBEV improves CMT's robustness against sensor asynchrony and mostly corrects the spatial misalignment of predicted boxes.
INTRODUCTION
Autonomous vehicles are typically equipped with multiple sensors such LiDAR, camera, and radar to support perception tasks (Caesar et al., 2020) de Groot et al. (025).. Temporal synchro- nization among these sensors is crucial for consistent multi-sensor fusion, and autonomous driving

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systems (Sun et al. 2020 Wilson et al. 2021 Palffy al. 2002) therefore employ various hardware- and software- based synchronization strategies (Sivrikaya & Yener 2004 Gurumadaiah et al. [025
Yuan et al. 2022; Faizullin et al., 2022). However, perfect synchronization is hard to guarantee in practice. First, sensors may operate at different sampling frequencies or cannot be triggered syn- chronously with other sensors (e.g. many radar sensors), hence time offsets between them cannot be avoided. Second, competition for computational resources among different parallel components of the processing pipeline can lead to spurious delays, leading to delayed or dropped frames (Fan al., 2025 Becker et al. 2020). Finally, common sensor failures, such as sensor crash or even ad adversarial attacks, may further exacerbate misalignment (Xie al., 2023 Shahriar et al.) 2025).
However, downstream multi-moda 3D object detectors are usually trained on well-synchronized and well-curated datasets, and do not consider the possibility of asynchronous inputs. Figure illustrates this: when one modality is delayed, detector pretrained on synchronized data produces spatial errors in bounding box predictions, which can compromise safety in autonomous driving.
Ego Motion Compensation (EMC) is widely used in autonomous driving to aggregate the temporal information to improve detection performance (Caesar et al., 2020 et al., 2022b; Wang et al.
2023b, Park et al. 2023 Cai et al.) 2023) Huang & Huang 2022 Yin et al. 2021). EMC can
compensate the spatial offset between sensor measurements at two time frames arising from the known movement of the ego vehicle, which can align cross moosl features of static objects, but it fails on dynamic objects at longer time intervals. Scene flow methods, on the other hand, estimate the motion of dynamic objects (Zhang et al.) (222a)), but they require two point clouds as input and assume fixed time offsets, which limits their applicability to asynchronous multi-modal settings.
To address sensor asynchrony, we propose AsyncBEV, lightweight and generic module that can be seamlessly integrated into diverse architectures of multi-modal 3D object detectors. AsyneBEV predicts the additional dynamic object motion not yet captured by EMC to align the asynchronous sensor data to reference timestamp. To this end, we define new task, A-BEVFlow estimation, which aims to predict the flow in the BEV space between multi-modal BEV features given the known time offset between the features. The offset- design of AsyncBEV facilitates the motion prediction for time offsets that can vary during operation, without significant performance loss in the synchronization case. Since AsyneBEV computes flow from BEV features, it does not make assumptions on the sensor modalities, nor which sensor is considered the reference. To summarize, main contributions
follows:
We introduce AsyncBEV, trainable and lightweight module that can be integrated into diverse multi-modal 3D object detector frameworks, and improves a detector's robustness against small, moderate, and severe time differences between sensors, especially for dynamic objects. We inte- grate ASy in both a tosen-based and dense grid-based detector architecture.
AsyneBEV utilizes A-BEVFlow, novel task that predicts flow in the BEV feature space. Unlike prior work scene flow, A A-BEVFlow operates on multi-modal feature maps rather than point clouds, and is conditioned on the known time interval between the sensors.
We study two different flow formulations: booion-based, which regresses the flow between BEV features directly, whereas velocity-based flow predicts object velocities, and afterward multiplies these with the known time interval to obtain motion. The velocity- based formulation regularizes the task, resulting in better performance than motion-based flow regression.
RELATED WORK
Multi-modal 3D Object Detection. To leverage the complementary information of different modalities (c.g., LiDAR and cameras), moulti-moda. 3D object detection has drawn wide attention in both academia and industry (Song et al. [0244b Wang et al. 2023c; Huang et al. 2022). Since cam- eras and LiDAR capture data in different coordinate systems, object detectors map the multi-modal features into shared reference system, usually LiDAR coordinates. They thus have to explicitly or implicitly project multi-view image features into the LiDAR (BEV) space. Grid-based methods
(Liu et al. 2023 Cai et al.) 2023 Wang et al., 2024 Ge et al. 2023 Li et al., 2022a) explicitly project image features into BEV grid for unified representations, while token-based methods (Yan
ct al. 2023: Chen et al. 2022 Li et al. 2022a) rely on object queries to implicitly perform coordi- nate transformation under the guidance of their positional encodings, which represent the mapping
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between each token and its 3D coordinate information perform the coordinate transformation implicitly. These methods require strict cross-senson synchronization so that the images and point clouds align in the 3D coordinate system, though 3D object detection under sensor asynchrony mains underexplored.
Scene Flow Estimation. Scene flow is the 3D motion field of all points in a scene, describing the per-point motion in 3D space between consecutive point clouds. Recent scene flow estimators deploy ferd-forward neural networks (FNN) achieve high accuracy and real-time inference (Jund) jet al., 2021] Zhang et al. 2024 20256, Kim et al. 2024 Lin et al. 2025: Zhang et al. (025a). Motion cues of dynamic objects, which scene flow provides, could help re-align information from the asynchronous time frames to a reference time frame. However, typical scene flow methods require two adjacent point clouds and assume fixed time offset, and are therefore not suited for aligning measurements from different sensor modalities at dynamic time offsets.
Asynchronous Fusion in Collaborative Perception. Asynchronous fusion is crucial problem in collaborative perception, as this task needs to fuse information from multiple agents. But dis- tinct agents and roadside units have independent clocks and are hard to synchronize with each other. CoBEVFlow (Wei et al. 2023) first generates object proposals from distinct agent features and then applies computation--ntenst
attention-based module to associate the proposals from different agents. Its flow is not directly learned but calculated based on the associated object proposals. UniV2X (Yu et al., 2025) employs an agent query-based flow predictor to generate the potential mo- tion of the query, which takes the associated agent from multiple frames input. Nevertheless, these flow calculation methods have two main shortcomings. First, their performance of flow generation heavily relies on the quality of the generated object proposals. However, in multi-modal (on-board) 3D object detection, the detection characteristics for different sensors can differ drastically. For instance, camera detectors usually perform worse than LiDAR detectors. Second, even assuming correct object proposals, in these two works, all agents send homogeneous features from the same modality (LiDAR features in CoBEVFlow (Wei et al. 2023) and image features in VniV (Yu) et al. )025)). The distinct features in multi-modal (on-board) 3D object detection pose further chal- lenges in associating object proposals generated from different sensors predicting query flows with a single MLP module. The effectiveness of their temporal alignment method has not been
validated in a multi-modal setup.
Asynchronous Fusion in On-board Perception. Asynchronous fusion in on-board perception
shares common concept with temporal feature aggregation, i.e., to align features from another timestamp to the reference timestamp. Early works (Caesar et al., 2020) Huang & Huang 2022
Park al. 2023) pioneered the use of EMC to align the static objects, while following works (Li et al. [022b] Wang et al. [202bb; Cai et al.] (202)) further apply attention modules to encode the object motion implicitly. StreamingFlow (Shi al. 2024) and TimeAlign (Song al., (024aa) pio- neered autonomous driving perception tasks with asynchronous input. However, both works deploy a computation- hhavy RNN-based module to predict features the reference timestamp, taking the long sequence history data as input. StreamingFlow (Shi et al.) 2024) takes streaming multi-modal features as a set of observations and treats these features uniformly, which requires a unified feature representation, such as grid-like BEV features, for both modalities. Hayes et al. (2024) leverages the velocity information provided by radars to update the point positions from the asynchronous timestamp to the reference timestamp, which shares the same spirit as scene flow estimation. Fan et al. (025) proposes directly integrating time features into per-point features to make the detector time-aware. None of the above methods explicitly uses the motion information from multi-modal features. Hence, to address the sensor asynchrony problem, we propose AsyncBEV, a lightweight, generic, interpretable and effective solution, which can work with arbitrary time offsets, asynchrony of different sensors, and distinct 3D object detectors.
METHODOLOGY
Most modti-modall 3D object detectors follow a similar structure, which we describe here first under the common simplifying assumption that all sensors are synchronous. This paper mainly considers camera and LiDAR fusion, though the same principles can be applied to include radar, too.
As input, the detector receives images II E gV Hmm XWmpx3 from V camera views, and a point cloud IP E ( First, I and P are fed into two separate backbones generate modal-specific
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Aeyychhhosss modal Objest Detection
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Figure 2: Overview of AsyncBEV. a) demonstrates an example pipeline of asynchronous multi- modal 31) object detection. this setup, images are sampled the reference timestamp 41 while LiDAR point clouds were obtained at earlier timestamp tg. b) shows the A-BEVFlow estimation, the core module of AsyncBEV. takes asynchronous BEV features as input and predicts the velocity of each BEV cell. The BEV flow is calculated by multiplying the velocity by At and supervised by the ground truth dense flow representation. Finally, the predicted flow is used to spatially align the features from la to with the detector-specific warper.
features in their native coordinate, resulting in multi-view image features in perspective view FP V De and LiDAR features bird's eye view F R.H.xW,.Dr, where He. Wc. De and H., WL, D2 represent the sizes and dimensions of perspective view (PV) image features and bird's eye view (BEV) LiDAR features, respectively. After getting the multi-modas features in their native coordinate systems FÇ and F different detectors deploy distinct strategies to map sensor features from sensor-specifie coordinates to shared 3D coordinate space. As LiDAR is already defined in the BEV space, these methods mainly differ in how to process camera features FL" to corresponding 3D coordinates CC. We shall distinguish two main categories of detectors, and show how to integrate AsyneBEV with each: grid-based and token-based methods.
Grid-based methods (Liu et al. 2023 Liang et al. 2022 Wang al. 2024) learn project F into a BEV grid with CDD: resulting in a dense grid-like camera BEV feature map FC FEEV, similar to the LiDAR feature map FLEV.
Token-basee methody eY 2023 Wang et al. 2023a) a sparse of object queries, which defined in the 3D space, retrieve information from features FC through set of 3D positional encoding generated from CG, This achieves the perspective to BEV projection implicitly.
Asynchrony. In practice, sensor input can be asynchronous, thus the timestamps associated with data from each sensor will differ. We assume that one sensor, e.g. the camera, is considered the reference that triggers inference of the multi-modal detector at timestamp t From the other sensor modality, e.g. LiDAR, the most up-to-date data from timestamp to is taken, which will be delayed w.r.t. the reference (to < t1). In this example, we would refer to the cameras as the synchronous sensor and the LiDAR the asynchronous sensor, and At offset. In both tosen- and grid-based frameworks, the asynchrony between sensors results in misalignment between CC.r, and CL.to which can lead to severe detection degradation, especially for important dynamic objects. Note that, hereafter, the sensor timestamp tg or t is indicated using superscripts on the corresponding symbols.
to the asynchrony time
In this work, we focus only cross-modality asynchrony, while asynchrony within multiple cameras is considered of scope.
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3.1 ASYNCBEV
We propose AsyncBEV to predict the flow (movement) of dynamic objects in the BEV space and then warp the misaligned BEV spatial locations before the detector's fusion. Figure 2 a) demon- strates how AsyncBEV can be integrated in a generic 3D object detection framework.
Flow estimation. basic step to align sensor coordinate frames and feature maps is Ego-Motion Compensation (EMC), which represents the known motion of the ego-vehicle between time and li, and thus aligns static objects only. the flow formulation, EMC can be represented as 2D flow field t E (N which expresses the relative spatial displacement for each BEV grid cell due to ego-motion. Alternatively, scene flow methods aim predict non-static motion too as a 3D point-level translation V E R (3) from two subsequent point clouds P E (N and pr+Ar E R(N"=A) .3, Therefore, current scene flow estimators can be generally formulated @scenetiow (IPr, Ptt+ Since existing scene flow methods require two
a function V(-++Ar point clouds as input and assume a fixed At, they cannot be directly used to address asynchronous multi-modal 3D object detection where Al can vary continuously.
To handle dynamic objects, AsyneBEV adapts the scene flow concept to a new task, A-BEVFlow. which should capture the motion missing from EMC. A-BEVFlow differs from prior scene flow works in several ways: 1) The flow is explicitly conditioned on an arbitrary time offset At, 2) it works with multi-modal BEV features instead of raw point clouds, 3) we only require 2D flow in BEV space. The task is thus to learn a function:
= AA-BEVFION
do BEV
F At),
BEV
where Vio-it E TR. (HaEY WBev represents the 2D flow field defined the BEV space with the size HBEV x WBEV, and FREV and F denote the BEV features of modality mn and my sampled from asynchronous timestamps tu and (1, respectively. We consider two possible formulations for motion estimation and velocity estimation.
Equation] |
The motion estimation (BEV-ME) formulation directly regresses the dynamic motion from At:
E d(cal C.C, FL.A0 BEV BEV
A()), V = =//(=>>..) (Eme))
Here Φ 1P +D++ R' ' is an encoder which compresses the (still misaligned) concatenated BEV features to lower-dimensional BEV feature map, and At, reducing memory and computa- tional requirements. Following common practice scene flow works, 1 is a U-Net (Ronneberger et al. 20155) which transforms the encoded BEV features into a flow field. We note (Fan et al.) 2025) recently also proposes to add At as additional input dimensions such that a detection network may learn to compensate for dynamic motion, although without explicit flow formulation.
We here propose an alternative formulation, velocity estimation (BEV-VE) Instead of regressing from At, this first predicts the velocity of each cell independent of At, and afterwards multiplies predicted velocities with At to obtain the requested motion, exploiting basic knowledge of the re- lation between motion, velocity and time. This design regularizes the flow at low At for synchronous sensors, since it forces the dynamic motion to become zero as At approaches zero, and simplifies the learning task:
Egee = p(cat (FaattFFCtt (FEV FL.)) )), Vio-tt
t (E..),
Via-st
via x At. vel
(3)
As our ablation study will validate, velocity estimation works better than motion estimation, and will be our default A-BEVFlow formulation in the other experiments.
Lightweight image BEV encoder. A-BEVFlow requires BEV features from all sensor modalities. Since token-basse 3D object detectors do not explicitly build image BEV features, we first generate the BEV features directly from the image features, ensuring AsyncBEV can work with different detection frameworks. We adopt SimpleBEV (Harley et al. 2023 to generate F SimpleBEV directly projects image features FC into BEV space with the cameras' extrinsic and intrinsic pa- rameters, avoiding demanding operations such as depth estimation (Liu et al. 2023 Liang et al.
2022 Huang et al. 2021) and deformable attention (Li et al. 2022b Wang et al. 2024
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Token-based 3D Object Detector
b) Grid-based 3D Object Detector
somple (warping)
FML
Miriis
M
Vier
vise mutiplication
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Figure 3: Defector-specifie Warper. Token-based methods rely 3D coordinates of tokens to provide spatial guidance. Generated A-BEVFlow is used to correct the corresponding 3D co- ordinates of tokens from the asynchronous sensor. b) Grid-based methods project the multi-modal features into predefined BEV grids. Generated A-BEVFlow used to build a look-up table from the reference timestamp the asynchronous timestamp warp the asynchronous BEV features to the reference timestamp.
Defector-specific warper. Finally, the ego-motion flow 6 and A-BEVFlow V need to be applied to align the features from different sensor modalities. The exact implementation of this step depends on the aforementioned detector architecture, token-based or grid-based.
Token-based methods encode spatial locations of tokens (sensor features) in their 3D positional encoding. Therefore, we directly adjust the 3D coordinates C%p of the asynchronous sensor m e {L, J} with the predicted flow BEY to obtain the tokens representing timestamp 4,, shown in Figure 3 a). Specifically, first apply the predicted dense flow Vla-+ to the position of each sparse token's positional embedding. We do the same operation for the EMC flow, also updating the token positions to compensate for the vehicle ego motion. The final 3D token coordinates Cyp are used generate positional encodings.
BEV
Grid-based methods implicitly encode spatial positions in the grid indices. Hence, here we can adopt grid sampling operation to look-up features in the BEV feature map for each cell in the BEV grid at 11- As shown in figure 3 b), we first define a standard BEV grid Ggev. representing the
3D space at f1, which should have the same size with BEV features F Then we simply add the ego-motion flow Md onto Gú compensate the ego-motion, resulting in GBE EMC We further calculate the final look-up table G learn the flow
G FMC V4u Note that this setup, we in fact flow -to instead of V'a-th as in the case of the token-based models. With G flow
obtain pscudo BEV features of modality tı, FEE with the "ample function, i.e., amn.4 = sample BEV BEV
Loss functions. AsyncBEV can be trained by back propagating through an existing frozen object detector framework, using the regular object detection losses. Following the common practice of 3D object detection losses, we also adopt focal loss for classification Les and L1 Loss for 3D bounding box regression Ireg- benefit of the explicit flow formulation that we can (optionally) also directly supervise it to facilitate training. For this, adopt the flow loss Lrow from Deflow (Zhang) jtt al. 2024),, using generated ground truth BEVFlow from annotated 3D object bounding boxes, as elaborated in Appendix A.9 This flow loss sums the mean L2-distance between the predicted and ground truth flows for three motion speed categories, ie. v < 0.4 m/s, 0.4 m/s < v S 1 m/s and v 1 m/s. The overall training objective is then weighted sum of the three components, Limal = W1 Lnow + W2 Let + W3 freg. where w1, w2, and w/g are hyperparameters that balance the contributions of the loss terms.
EXPERIMENTS
Dataset. We conduct experiments on the nuScenes (Caesar et al. 2020 dataset. nuScenes is large-scale autonomous driving dataset collected in Boston and Singapore. It has 1000 scenes in total and is divided into train/val/test sets with 750/150/15 scenes. nuScenes contains multi- modal data from 6 cameras, LiDAR, and 5 radars. In this work, mainly focus LiDAR and

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All Objects
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Method
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71.5 143
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63.5
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41.2
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32.9
29.6
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25.3
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NOS 66.7
62.0
53.8
61.7
58.9
48.2
63.5
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62.9
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47.4
618
49.9
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43.2
37.3
52.1
114
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39.4.1 155
63.3
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61.3 107
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Table 1: Evaluation on the nuScenes val set of asynchronous multi modal 3D object detection (Best, Second Best). LiDAR is asynchronous. AsyncBEV is only trained once with each model and evaluated on different time offsets. FD: freeze the whole detector and only train AsyncBEV. Performance deltas are measured relative to the EMC baseline. Inference speed in Frames Per Second (FPS) is measured with single A100 GPU.
camera fusion for 3D object detection. Following common practice in nuScenes, we report both the nuScenes Detection Score (NDS) and mean Average Precision (mAP) as metrics. To understand the motion compensation performance of our approach, we further report NDS and mAP for dynamic and static objects. We adopt 0.2 m/s as threshold to distinguish dynamic and static objects in both predictions and ground truth, then calculate the metrics respectively.
Baselines. We integrate AsyncBEV in both the CMT (Yan et al. 2023) and UniBEV (Wang al. 2024) architectures. which are representative for token-basee and grid-based multi-modal 3D ob- ject detectors. Compared to its original end-to-end training schema, we retrained UniBEV with a two-stage training protocol to accelerate the training process. In the default setting, we use frozen pretrained backbones and finetune only the AsyncBEV module and the detection heads unless oth- erwise specified. We also train variant AsyncBEV (FD) of each architecture with frozen detection heads, so only the AsyncBEV module is trained. We adopt a learning rate of x 10-5 for finetun- ing in both architectures. Other hyperparameters are kept the same as their original training setups. For both CMT and UniBEV architectures, we also test an EMC-only variation without AsyncBEV, which has additional learnable components. Data augmentation (DA) is another simple base- line for both networks, which directly finetunes the vanilla detectors with the same asynchronous training scheme as AsyneBEV.
Furthermore, we also deploy the two asynchronous fusion methods Fan et al. 20255 and Stream- ingFlow (Shi al., 2024) as baselines. [an al. (2025) proposes a temporal information propaga- tion method for point clouds. Modern LIDAR encoders usually accumulate multiple historic sweeps of point clouds with the latest one as input point cloud, and append their time offsets relative the latest one as an additional temporal feature alongside its geometric dimensions. Following Fan et al. (225), we simply replace the original temporal feature with the asynchronous time offsets At mea- sured relative to the reference timestamp. We integrate Fan et al. (2025) with CMT for asynchronous Faa
LiDAR. Please note, Fan et al. (2025) can only be applied to the point cloud input, as images usu- ally have no time dimension. A reasonable extension is to concatenate the At to the intermediate features, which is similar to our motion-base flow estimation variant. StreamingFlow utilizes a GRU-ODE to model the temporal dynamics among gike features from different timestamps. To adapt StreamingFlow to 3D object detection task, we therefore integrate the GRU-ODE module into our grid baseline, UniBEV, For all the trainable baselines, also adopt the same training
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scheme with AsyneBEV, i.e., keep the pretrained backbone frozen and finetune the detection head and other components.
Asynchronous training setup. While our method is generic to any dataset, we follow nuScenes conventions here for simplicity. As is common practice, we train and evaluate our method anno- tated nuScenes keyframes at 2 Hz. Considering the keyframe as the reference timestamp, one sensor synchronous while the other sensor asynchronous. Since the nuScenes dataset artificially cu- rated to discard scenes with bad synchronization, we cannot use the natural data, but have to impute asynchronous data by using older sensor data for the asynchronous sensor. During training we vary the time offset At uniformly at random between 0 and maximum of 0.5 s. We consider 0.5 the worst-case scenario for our robustness test, which could be uncommon but high-impact. For the asynchronous sensor we use the sensor data that precedes the reference timestamp by the specified time offset. The cameras have a frequency of 12 Hz and the LiDAR operates at 20 Hz. Since we cannot realistically infer the sensor data at arbitrary point in time, we instead use the closest asyn- chronous sensor data to the specified time offset. Therefore we cannot evaluate arbitrarily small time offsets, but only multiples of the sensor period (83 ms for camera and 50 for LiDAR). nuScenes uses 6 cameras which are triggered when the LiDAR scans across the center of each camera's 's Field of View (Fo Therefore each camera has different timestamp. We treat all cameras the same way and assign them the timestamp of the back-left camera, which is closest to the reference timestamp of the corresponding LiDAR sweep.
4.1 ASYNCHRONOUS MULTI- 3D OBJECT DETECTION
Table 1 presents the main evaluation results for large time intervals (severe asynchrony), using the camera as the reference time frame while LiDAR is asynchronous with it. A similar table where LiDAR is synchronous and cameras asynchronous can be found in Appendix [A.]] demonstrating the generality of AsyncBEV. Since both setups show similar trends, we focus here on the camera as the reference setup only. only, at lower time offset ranges, representing more common less severe cases. similar figure for mAP is shown in Appendix [A.6] Note that the last column of Table | showcases the FPS of each method, which confirms our AsyneBEV only introduces marginal latency to the vanilla models.
the NDS for All Objects, and for Dynamic Objects
Vanilla Models. When the asynchrony be- tween images and point clouds increases to 0.5 8, the performance of both 3D object de- tectors, CMT and UniBEV, decreases dramat- ically, confirming sensor asynchrony leads to performance degradation if not properly ac- counted for. Especially, the performance of Dynamic Objects declines significantly from 47.5% NDS to 26.1% for CMT and 42.4% NDS to 25.3% for UniBEV with 0.5 time off- set. Even with mild sensor asynchrony with 50ms (0.05s) time interval, its performance for All Objects and Dynamic Objects drops sub- stantially, as shown in Figure
80
70-
NDS
40
CMT (All
CMT+EMC
AMT4AyeebBBV Wil
30
CMT (Dynamic only
CHT+EM EMC (Dynamic-onty)
20
CMT+ Apye EV Dyramic-onyy
CMT+ AsyncSEV (ill min
CM144 dyrmiic-orlyy
10
50
100
150 200 250 300 350 400 450 500 time offset (ms)
Ego Motion Compensation (EMC) only. cable[]conffrmms only performing EMC already significantly improves the NDS with 0.5s asyn- chrony for CMT and UniBEV. Notably, the per- formance of static objects at 0.5 asynchrony improves by 23.3% NDS and to 67.5% for CMT and by 21.0% NDS to 62.2%, which reaches a comparable performance of static objects in the synchronous case with 69.0% and 63.5% for CMT and UniBEV. However, EMC does not bring a performance improvement for dynamic objects, as expected. Similar observations can be made in Figure Though the performance of EMC improves the performance for All Objects over the vanilla CMT across all time offsets, its performance for Dynamic Objects barely increases.
Figure 4: Performance of different methods with increasing time offsets under LiDAR asynchrony.
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Data Augmentation. Finetuning the vanilla model with asynchronous inputs as data aug- mentation (DA) strategy also improves robustness to sensor asynchrony, but its effectiveness dif- fers between CMT and UniBEV. Compared to CMT+EMC, the performance of CMT+DA in the synchronous case declines by 1.4% NDS but increases in asynchronous scenarios. In contrast, UniBEV+DA underperforms UniBEV+EMC in both cases. Notably, DA consistently improves the performance for Dynamic Objects of both models over the EMC baselines.
AsyncBEV. In Table [ the CMT variants overall outperform UniBEV variants, but AsyneBEV helps improve asynchrony robustness for both types of architectures. At a time offset of 0.5 8, CMT+AsyncBEV and UniBEV+AsyncBE) reach 70.0% and 63.3% NDS respectively, sur- passing their EMC counterparts with a margin of 6.7% NDS and 12.3% mAP. Notably, both CVV and UniBEV+AsyncBEy namely 43.4% and 37.8% NDS, which significantly outperforms their counterparts with EMC with a margin of 16.6% and 11.9% NDS, and the vanilla models with a considerable margin of 17.3%. For static objects, CMT+AyncBEV performs comparably to CMT+EMC, as expected.
achieve strong performance for Dynamic Objects,
We also observe that the Asy can introduce a slight performance degradation in the synchro- nized case (0 s) for both models, in its default setting where we fine-tune the detector head, NDS for all objects shows modest drop of 0.4% for CMT and 1.0% for UniBEV. However, AsyncBEV (FD) maintains the performance of the vanilla models in this synchronized setting. In this variant all orig- inal detector components are frozen, and the velocity-based formulation ensures that for A/ = no motion is predicted, thus all BEV features remain unaffected. Still, AsyncBEV (FD) underper- forms compared to default Asy on Dynamic Objects in asynchronous cases, due to fewer trainable parameters. Fine-tuning the detector head when training AsyncBEV thus controls trade- off between these cases. Given the modest effect on synchronous cases and strong improvements on asynchronous cases, we keep AsyneBEV with fine-tuning the detector heads as the default.
Figure confirms AsyncBEV improves the robustness of the vanilla CMT also for smaller time intervals, showing flatter curves for both categories. Notably, the performance for All Objects of AMT+AsyncBEV in an extreme sensor asynchrony case, i.e., 0.5 8 time offset, outperforms vanilla CMT with mild 50 ms time offset.
Other Baselines. Fan et al. (2025) propagates temporal features, which expects the network to align the features implicitly according to the given asynchronous time offset. As shown in Table T] this strategy mitigates some of the misalignment for higher time offsets compared to the EMC baseline. On the other hand, it also exhibits significant drop in performance for the 0 compared to vanilla CMT. Overall its performance is inferior to our proposed AsyncBEV for all time offsets, with the performance gap widening as the time offset increases.
StreamingFlow takes a sequence of past images or LiDAR frames as input, and predicts features for a reference timestamp. Despite its performance in synchronous scenarios drops 2.5% NDS, StreamingFlow improves performance for asynchronous scenarios compared to the vanilla UniBEV. Nevertheless, in both scenarios StreamingFlow still underperforms our AsyncBEV, which only uses the latest asynchronous observations as input. Furthermore, due to the need to process every ob- servation in the input sequence with the GRU-ODE module, StreamingFlow operates significantly slower than AsV in terms of FPS.
Qualitative Results. A benefit of our flow prediction approach that such visualizations im- prove the interpretability of AsyncBEV. Figure 5 showcases the qualitative results of CMT and CMT+AsyncBEV under the extreme LiDAR asynchrony case with 0.5 s time offset. In Figure 5 a), the object in the highlight box cannot be detected by the original CMT due to large spatial misalignment in the ahennhroone asynchronous sensor information. Predicted A-BEV Figure (5c) aligns with the GT flow shown in Figure 5 d) and matches the spatial offsets between the predicted box and the ground truth box. Thanks to the motion cue provided by A --BEVFlow, CMT+AsyncBEV successfully detected the object with small translation error, as shown in F)g
To sum up. AsyneBEV brings significant performance gains for 3D object detectors under both severe and light sensor asynchrony, especially for dynamic objects. The approach is generic and lightweight, improving detector robustness for arbitrary time offsets during operation, supporting different sensor modalities, and distinct 3D object detection architectures.

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Published as a conference paper at ICLR 2026
CMT
b) CMT-ASyncBEV (ours)
predicted A-BEVFlow
GT 4-BEVFlow
Figure 5:
Qualitative Results of AsyncBEV. The left two columns demonstrate the 3D object detection performance of CMT and CMT+AsyncBEV under 0.5s LiDAR asynchrony. The right two columns show the output our predicted A-BEVFlow and the ground truth A-BEVFlow.
4.2 ABLATION STUDY OF A-BEVFLOW STRATEGY
In this ablation study, we focus UniBEV only, as performance trends on UniBEV and CMT are gen- erally similar. Tame pares the motion-based for- mulation to the (default) velocity-based formulation of A-BEVFlow, and the impact of exposing the model to varying delays during training. The orig- inal UniBEV performs well in synchronized case, but fails catastrophically when serious asyn- chrony occurs. Based on UniBEV+EMC, we finetune UniBEV with the asynchronous data (EMC+ + DA), which brings significant improvement for the asynchronous case, but also degrades the perfor- mance in the synchronized case. Motion- based flow estimation (BEV-ME) helps compensate for the dynamic objects. Velocity- based flow estimation (BEV-VE) still further prevents the performance drop in the synchronization case, and strengthens the results under extreme sensor asynchrony, val- idating our choice for this formulation default.
A-BEVFlow design Motion
All Objects (0.))
All Objects (0.5 8)
EMC+DA
Velocity
NDS
mAP
NDS
mAP
X
X
66.7
62.0
39.7
22.2
X
X
63.1
57.6
61.1
54,9 1317
V
X
64.2
58.6
62.5
122
56.1 1339
X
V
65.7 110
60.8
63.3
57.1
Table 2: Ablation study of A-BEVFlow design. This table showcases the core design choice for the new task A-BEVFlow. EMC+DA: EMC + asynchronous data augmentation training. Mo- tion: BEV-ME. Velocity: BEV-VE, which is our default design.
CONCLUSIONS
We have proposed AsyncBEV, lightweight and generic module to improve the robustness of multi-modal 3D object detector against sensor asynchrony. Generalizing the scene flow concept, we first propose novel task, A-BEVFlow. which predicts the flow in the BEV space given arbitrary time offset for multi- modal BEV features. The predicted flow can then be applied to spatially align the feature maps of the asynchronous sensor to the reference frame. Our experiments show AsyncBEV significantly improves robustness under sensor asynchrony for both token-based and grid-based detectors, with only little computational overhead. Under an extreme LiDAR asyn- chrony case with 0.5 time offset, AsyncBEV improves the performance for All Objects of vanilla methods with considerable margin of 26.8% NDS on token-based CMT and 23.6% NDS on grid- based UniBEV. Particularly. AsyneBEV outperforms EMC for Dynamic Objects with 16.6% NDS on CMT and 11.9% NDS on UniBEV. Similar trends are observed for smaller time offsets. We also show relocity-based formulation for the A-BEVFlow estimator helps regularize the predicted motion of dynamic objects over directly predicting the motion.
nuScenes
Limitations and Future Work. In this work, we assume that one sensor is always synchronized with the reference frame, while the other may have time offsets. However, in the real autonomous be asynchronous
driving application, there be more than two sensors, of which multiple once. Please refer to Appendix AA7 more details. In the future, we will develop a ftll-streaming framework always fuse the most up-to-date data from multiple sensors, including radar.
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ACKNOWLEDGMENTS
This work was supported by the 3D Urban Understanding (3DUU) Lab funded by the TU Delft AI Initiative.
REFERENCES
Pedro HE Becker, Jose Maria Armau, and Antonio González. Demystifying power and performance bottlenecks in autonomous driving systems. In 2020 IEEE International Symposium on Workload Characterization (IISWC), 2020.
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuScenes: multimodal dataset for autonomous driving. In CVPR, 2020.
Hongxiang Cai. Zeyuan Zhang, Zhenyu Zhou, Ziyin Li, Wenbo Ding, and Jiuhua Zhao. BEV- Fusion4D:: Learning lidar-camera fusion under bird's seee--veww via cross-modality guidance and temporal aggregation. arXiv preprint arXiv:2303. 17099, 2023.
Xuanyao Chen, Tianyuan Zhang, Yue Wang, Yilun Wang, and Hang Zhao. Futr3d: A unified sensor fusion framework for 3d detection. In CVPR, 2022.
Oscar de Groot, Alberto Bertipaglia, Hidde Boekema, Vishrut Jain, Marcell Kegl, Varun Kotian, Ted Lentsch, Yancong Lin, Chrysovalanto Messiou, Emma Schippers, et al. A vehicle system for navigating among vulnerable road users including remote operation. In IV, 2025.
Marsel Faizullin, Anastasiia Komilova, and Gonzalo Ferrer. Open-source lidar time synchronization system by mimicking goss-clock. In ISPCS, 2022.
Meng Fan, Yifan Zuo, Patrick Blaes, Harley Montgomery, and Subhasis Das. Robust sensor fusion against on-vehicle sensor staleness. In CVPRW, 2025.
Chongjian Ge, Junsong Chen, Enze Xie, Zhongdao Wang, Lanqing Hong. Huchuan Lu, Zhenguo Li, and Ping Luo. MetaBEV: Solving sensor failures for bev detection and map segmentation. In CVPR, 2023.
Ajay Kumar Gurumadaiah, Jaehyeong Park, Jin-Hee Lee, JeSeok Kim, and Soon Kwon. Precise synchronization between lidar and multiple cameras for autonomous driving: An adaptive ap- proach. IEEE T-IV, 2025.
Adam W. Harley, Zhaoyuan Fang, Jie Li, Rares Ambrus, and Katerina Fragkiadaki. Simple-BEV: What really matters for multi-sensor bev perception? In ICRA, 2023.
Seamie Hayes, Sushil Sharma, and Ciarán Eising. Velocity driven vision: asynchronous sensor fusion birds eye view models for autonomous vehicles. In IET Conference Proceedings CP887, 2024.
Junjie Huang and Guan Huang. BEVDe14D: Exploit temporal cues in multi-camera 3d object de- tection. arXiv preprint 220::2203./705:4 2022.
Junjie Huang, Guan Huang, Zheng Zhu, Ye Yun, and Dalong Du. BEVDet: High-performance multi-camera 3d object detection in bird-eye-view. arXiv preprint arXiv:2112.11790, 2021.
Keli Huang, Botian Shi, Xiang Li, Xin Li, Siyuan Huang, and Yikang Li. Multi-modal sensor fusion for auto driving perception: A survey. arXiv preprint arXiv:2202.022700 2022.
Philipp Jund, Chris Sweeney, Nichola Abdo, Zhifeng Chen, and Jonathon Shlens. Scalable scene flow from point clouds in the real world. RA-L, 2021.
Jaeyeul Kim, Jungwan Woo, Ukcheol Shin, Jean Oh, and Sunghoon Im. Flow4d: Leveraging 4d voxel network for lidar scene flow estimation. RA-L, 2024.
Yanwei Li, Yilun Chen, Xiaojuan Qi, Zeming Li, Jian Sun, and Jiaya Jia. Unifying Voxel-based Representation with Transformer for 3D Object Detection. In NewrIPS, 2022a.

---

Published as a conference paper at ICLR 2026
Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Yu Qiao, and Jifeng Dai. BEVFormer: Learning bird's wod's-eye-view representation from multi camera images via spa- tiotemporal transformers. In ECCV, 2022b.
Tingting Liang, Hongwei Xie, Kaicheng Yu, Zhongyu Xia, Zhiwei Lin, Yongtao Wang, Tao Tang. Bing Wang, and Zhi Tang. BEVFusion: A simple and robust lidar-camera fusion framework. In NeurIPS, 2022.
Yancong Lin, Shiming Wang, Liangliang Nan, Julian Kooij, and Holger Caesar. VoteFlow: Enfore- ing local rigidity in sulf-supe scene flow. In CVPR, 2025.
Zhijian Liu, Haotian Tang, Alexander Amini, Xingyu Yang, Huizi Mao, Daniela Rus, and Song Han. BEVFusion: Multi-task multi-sensor fusion with unified bird's- s-eye view representation. In ICRA, 2023.
Andras Palffy, Ewoud Pool, Srimannarayana Baratam, Julian Kooij, and Dariu Gavrila. Multi-class Road User Detection with 3+1D Radar in the View-of-Delle Dataset. RA-L, 2022.
Jinhyung Park, Chenfeng Xu, Shijia Yang, Kurt Keutzer, Kris Kitani, Masayoshi Tomizuka, and Wei Zhan. Time will tell: New outlooks and baseline for temporal multi-view object detection. In ICLR, 2023.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomed- ical image segmentation. In MICCAI, 2015.
Md Hasan Shahriar, Md Mohaimin Al Barat, Harshavardhan Sundar, Naren Ramakrishnan, Y Thomas Hou, and Wenjing Lou. On the fragility of multimodal perception to temporal mis- alignment in autonomous driving. arXiv preprint arXiv:2507.09092 2025.
Yining Shi, Kun Jiang. Ke Wang, Jiusi Li, Yunlong Wang, Mengmeng Yang, and Diange Yang- Streamingflow: Streaming occupancy forecasting with asynchronous multi-modal data streams via neural ordinary differential equation. In CVPR, 2024.
Fikret Sivrikaya and Bülent Yener. Time synchronization in sensor networks: a survey. IEEE network, 2004.
Zhihang Song. Lihui Peng, Jianming Hu, Danya Yao, and Yi Zhang. Timealign: A multi-modal object detection method for time misalignment fusing in autonomous driving, 2024a.
Ziying Song, Lin Liu, Feiyang Jia, Yadan Luo, Caiyan Jia, Guoxin Zhang, Lei Yang, and Li Wang- Robustness-aware 3d object detection in autonomous driving: A review and outlook. IEEE Trans- actions on Intelligent Transportation Systems, 2024b.
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for au- tonomous driving: Waymo open dataset. In CVPR, 2020.
Haiyang Wang, Hao Tang, Shaoshuai Shi, Aoxue Li, Zhenguo Li, Bernt Schiele, and Liwei Wang- Unitr: A unified and efficient multi-modal transformer for bird' sird'--eee-viww representation. In ICCV, 2023a.
Shihao Wang, Yingfei Liu, Tiancai Wang. Ying Li, and Xiangyu Zhang. Exploring object-centric temporal modeling for efficient multi-view 3d object detection. In ICCV, 2023b.
Shiming Wang, Holger Caesar, Liangliang Nan, and Julian FP Kooij. Unibev: Multi-modal 3d object detection with uniform bev encoders for robustness against missing sensor modalities. In IV. 2024.
Yingjie Wang, Qiuyu Mao, Hanqi Zhu, Jiajun Deng, Yu Zhang, Jianmin Ji, Houqiang Li, and Yany- ong Zhang. Multi-modal 3d object detection in autonomous driving: survey. IJCV, 2023c.
Sizhe Wei. Yuxi Wei, Yue Hu, Yifan Lu, Yiqi Zhong, Siheng Chen, and Ya Zhang. Asynchrony- robust collaborative perception via bird's eye view flow. In NeurIPS, 2023.
12

---

Published as a conference paper at ICLR 2026
Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandel- wal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, and James Hays. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In NeurlPS, 2021.
Shaoyuan Xie, Lingdong Kong, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. Robobev: Towards robust bird's eye view perception under corruptions. TPAMI, 2023.
Junjie Yan, Yingfei Liu, Jianjian Sun, Fan Jia, Shuailin Li, Tiancai Wang, and Xiangyu Zhang. Cross modal transformer via coordinates encoding for 3d object detection. In ICCV, 2023.
Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center-based 3d object detection and tracking. In CVPR, 2021.
Haibao Yu, Wenxian Yang. Jiaru Zhong, Zhenwei Yang, Siqi Fan, Ping Luo, and Zaiqing Nie. End- to-end autonomous driving through v2x cooperation. In AAAI, 2025.
Kaiwen Yuan, Li Ding, Mazen Abdelfartah, and Z Jane Wang. Licas3: simple lidar-camera self-supervised synchronization method. JEEE T-RO, 2022.
Qingwen Zhang, Yi Yang. Heng Fang, Ruoyu Geng, and Patric Jensfelt. Deflow: Decoder of scene flow network in autonomous driving. In ICRA, 2024.
Qingwen Zhang, Ajinkya Khoche, Yi Yang, Li Ling, Sina Sharif Mansouri, Olov Andersson, and Patric Jensfelt. Himo: High-speed objects motion compensation in point clouds. T-RO, 2025a.
Qingwen Zhang, Yang, Peizheng Li, Olov Andersson, and Patric Jensfelt. Seflow: A self- supervised scene flow method in autonomous driving. In ECCV, 2025b.
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A APPENDIX
A.1
MULTI-MODAL 3D OBJECT DETECTION WITH ASYNCHRONOUS CAMERAS
Objects
Static Objects
Dynamic Objects
Mothod
0
0.25
0.5
0
0.25
0.5
0
0.25
0.5
NDS
72.9
70.7
67.5
69.0
66.9
63.8
47.5
46.9
45.8
CMT
MAP1
66.7
61.0
52.4
36.0
NDS
72.9
71.4
68.8
69.0
67.6
65.1
47.5
46.9
45.9
MAP
70.3
67.8
63.5
61.0
58.9
54.9
36.8
36.0
34.1
CMT+AsyncBEV
NDS
72.7 Lo2
72.7
72.3 115
112
48.1 1.0
48.1
48.0
mAP
69.8 Los
69.7
19
69.1
136
60.8
60.6 1:17
60.2
37.0 10
36.9
36.7 13
UniREV
NDS
66.7
64.8
62.7
63.5
61.9
60.0
42.4
41.8
40.9
m
62.0
59.3
56.1
52.2
47.1
32.9
32.3
30.6
UNIBEV-EMC
NDS
66.7
66.5
66.2
63.5
63.5
63.2
42.4
41.9
41.0
mAP1
32.2
UniBEV+AsycBBE
NDS
66.9
66.8
66.7
63.4
63.3
63.2
42.0
42.1
42.2
+13
mAP
62.0
61.8
61.5
51.7
Los
51.6 1oc
51.5 1.
32.3
L.6
32.3 1.0
32.3 1.
Table III: Evaluation the nuScenes val set of asynchronous multi modal 3D object detection. (Best, Second Best). Camera is asynchronous. Trained with one model and evaluated on different time offsets. Performance deltas are measured relative the EMC baseline.
Table III demonstrates an opposite asynchrony, i.e., LiDAR is on the reference timestamp, but cam- eras are asynchronous, Because the dominating sensor, LiDAR, is still functioning, the performance degradation is not as significant as in Table I We can still observe that NDS for All Objects de- creases 5.4% to 67.5% for CMT and 5.0% to 62.7% for UniBEV with 0.5 asynchronous time offset, respectively. AsyncBEV improves the performance of CMT and UniBEV to 72.3% and 66.7% NDS, respectively. Table validates the generality of our proposed AsyncBEV.
A.2 COMPARISON WITH THE SENSOR-FALLBACE STRATEGY
All Objects
Static Objects
Dynamic Objects
Method
NDS
mAP
NDS f
mAP
NDS t
mAP t
CMT L
43.2
25.8
44.2
22.8
26.1
9.0
CMT-C
44.7 115
38.3 1:2
47.4 632
29.5 767
28.3 r22
14.7 133
CMT+AsyncBEV LL
70.0 253
66.4 28.1
67.6 + 202
59.1 1.2.6
43.4 1
32.3 176
UniBEV L)
39.7
22.2
41.2
19.0
25.3
8.9
UniBEV
42.6 122
35.4 132
46.1 +49
27.1 1.8.1
28.2 +28
13.1 1:42
UniBEV+AsyneBEY s
63.3 2.07
57.1 121.7
61.5 15A
49.1 2200
37.8 196
26.6 135
CMT (0.5
67.5
61.2
63.8
52,4
45.8
34.2
CMT I.
68.1 1.6
61.7
64.1 603
53.0 1.6
46.7 1.9
34.5
CMT+AsyncBEV (0.5
72.3 142
69.1 174
68.4
60.2 472
48.0 13
36.7 1:2
UniBEV (
62.7
56.1
60.0
47.1
40.9
30.6
UniBEV L
63.7 110
56.
60.5 nos
47.7 9.6
42.2 113
31.7 1M
UniBEV+AsyncBEV ( C)
66.7 1:300
61.5 1.5.4
63.2 227
51.5 7388
42.2
32.3 p.6
Table IV: Performance of single-modality performance CMT and UniBEV.
In our 0.25 and 0.5 delay setups, discarding stale data (> 100 ms) is equivalent to single- modality inference. Table IV compares our AsyncBEV to single-modality. As shown in Table IV when LiDAR is delayed and a sensor fallback strategy is adopted, camera-only CMT-C achieves 44.7% NDS while camera-only UniBEV C performs at 42.6% NDS. Both outperform the vanilla CMT (43.2% 43.2% NDS) and UniBEV (39. 79. NDS) under the 0.5 LiDAR asynchrony. This shows that the sensor- fallback strategy can serve as backup option in cases of severe sensor asynchrony, even though the performance gains remain minor. However, CMT+AsyncBEV achieves 70.0% NDS, and UniBEV achieves 63.3% NDS even with an extreme 0.5s time offset for LiDAR, outperforming the single-modality performance significantly.
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We make similar observations for the camera-delayed scenario. With a 0.5 time offset for cameras,
CMT+AsyncBEV achieves 72.3% NDS, while UniBEV+AsyncBEV achieves 66.7% NDS, both surpassing the LiDAR-only performance of CMT L (68.1% NDS) and UniBEV L (63.7% NDS). This shows that the asynchronous modality still contains rich information for perception tasks. In conclusion, effectively leveraging temporally misaligned information is better option than com- pletely dropping them.
A.3 MORE QUALITATIVE RESULTS
CMT
b) CMT+AsyncBEV (ours)
predicted 4-BEVFlow
d) GT 4-BEVFIOw
stnden
0
Figure VI: Qualitative Results of AsyneBEV. The left columns demonstrate the 3D object detection performance of CMT and CMT+ AsyncBEV under LiDAR asynchrony with 0.5 offset. The right two columns show the output of our BEV-VE module and the ground truth flow in the BEV space.
This sample shows driving scenario in parking lot, in which most of the objects static. Detection of CMT introduces large translation errors for predictions due to the ego vehicle motion. CMT+AsyncBEV corrects most of the static boxes and re-aligns the moving long bus. The color and saturation of the predicted flow align with the GT flow, which validates the interpretability of our proposed AsyncBEV.
A.4 ABLATION OF BEV- VE DESIGN.
Table V ablates the effect of whether to use the multi-modal features (MM) and the flow loss (L) with the performance of all objects and dy- namic objects under the 0.5 time offset. The basic setting the first row also applies the concept of BEV-VE to explicitly predict the dy- namic object flow and then warp the feature- coordinate mapping, but only takes the asyn- chronous features as input and is supervised by the detection loss in end-to-end manner. The basic setting already achieves good perfor- mance. Using multi-modal features (MM) and the flow loss (Loss) for supervision further improves the performance under asynchrony with small margin.
All Objects (0.5s)
Dynamic Objects (0.5s)
Loss
MM
NDS
mAP
NDS
mAP
x
63.0
56.8
37,0
25.4
x
63.2
57.0
37.6
26.0
63.3 ta1
57.1
37.8
26.6
Table V: Ablation of BEV-VE inputs. MM: Multi- modal features, Flow loss.
A.5 ABLATION OF IMAGE BEV FEATURES.
Are the BEV features from SimpleBEV still necessary for grid-based 3D object detector, which already builds BEV features from the multi-view image features? Table VI ablates the effect of the separate BEV encoder, which provides performance on All Objects and Dy- namic Objects of UniBEV under LiDAR asyn- chrony with a 0.5 time offset. The first row shows the basic setting, in which BEV-VE
All Objects (0.5 s)
Dynamic Ohjects(0.55 x)
SimpleBEV
NDS
mAP
NDS
mAP
62.7
56.1
37,3
25.7
63.3
57.1
37.8
26.6
Table VI: Ablation of SimpleBEV encoder
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Published as a conference paper at ICLR 2026
directly takes the BEV features for 3D object
detection as input, while the second row showcases that an extra SimpleBEV encoder is used spe- cially for the A-BEVFlow estimation. Despite the basic setting already achieving good performance with 62.7% NDS for All Objects and 37.3% NDS for Dynamic Objects, its counterpart with Sim- pleBEV consistently improves the performance with small margin.
A.6 PERFORMANCE DEGRADATION FOR INCREASING TIME OFFSETS
Figure VII further demonstrates the curves of mAP with increasing time offsets for three methods, vanilla CMT, CMT+EMC and CMT+AsyncBEV, which shares the same trend with Figure Performance on all objects and dynamic objects is reported in the figure. CMT+AsyncBEV has the most robust perfor- mance against sensor asynchrony. Its perfor- mance in the extreme case with 0.5 is bet- than vanilla CMT with one-frame (50 ms) asynchrony. Despite CMT+EMC delivering improved performance for All Objects, its per- formance for Dynamic Objects still shares the same degradation with vanilla CMT, which proves the necessity of module further compensate for the dynamic object motion.
80
70
60-
50-
MAP
40
30
20
CMT (Sponinsstt
CM ENC 1Smamiccrty
10
CR Agy
100 150
250 300
450 500
time offset (ms)
Figure VII: Performance of CMT, CMT+EMC and AsynAEyy for different time offsets under LiDAR asynchrony.
A.7 EXTENSION MULTI-SENSOR ASYNCHRONY
The AsyncBEV framework can be extended to multi-sensor asynchrony scenarios. We pre- sume have one reference sensor (e.g., LiDARR and treat all other (asynchronous) sensors (e.g., cameras and radars) as paired to the LiDAR reference only. This approach requires an additional lightweight AsyneBEV module for each additional asynchronous sensor modality, since network weights can be shared across sensors of the same modality. Possibly, multiple sensors of the same modality could be batched together (with a vector containing all their At) in a single forward pass to compute their A BEVFlow in parallel.
For the multi-sensor asynchrony scenario, the ad- ditional runtime and memory of AsyneBEV is negligible compared to the dominant cost from pro- cessing the extra sensor inputs. As shown in Ta- ble [ AsyncBEV reduces the inference speed of CMT by only 0.4 FPS, and this overhead drops to 0.1 FPS when applied to heavier baseline such as UniBEV. Furthermore, Table [VI[ shows that the camera branch has 232x the parameters of AsyneBEV, while the LiDAR branch has 23x. Therefore, when scaling to setups with more sensors, the backbone becomes substantially heavier, and the marginal runtime and memory overhead introduced by AsyncBEV becomes negligible compared to the total computation.
Module
# Params
x XAsyncBEE
Camera branch
70, 572, 864
232
LiDAR branch
6, 975, 184
AsyncBEV
304, 114
Table VII: Params of CMT+AsyncBEV's modules. XASyneBEV the parameter ratio of each module relative to AsyncBEV.
Finally, note experiments use all six cameras from nuScenes, treating them as single sensor. Indeed, performance might futher improve if they are handled all independently, but our results already show the benefit of our approach even by grouping related asynchronous sensors.
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Published as a conference paper at ICLR 2026
A.8 DENSE GT FLOW REPRESENTATION
The supervised scene flow task takes the ground truth flow as supervision to calculate a per-point flow for the source point cloud. The ground truth flow is usually generated by calculating the motion between bounding boxes (Jund et al. 202T] Zhang et al. 2024). Hence, this flow representation in- herits the sparsity of point clouds. As point clouds usually only capture partial objects, this per-point sparse flow representation cannot fully present the object motion in a dense BEV grid. Therefore, we propose dense ground truth flow representation, dense flow, which only relies on bounding boxes. By decoupling from point clouds, dense flow facilitates generating ground truth flow for camera BEV features.
The process to generate dense flow is shown in Algorithm 1 the generation of scene flow ground truth are that we use a dense standard grid GBEV to represent the dense and evenly distributed point cloud instead of using the real point cloud. Then adopt ElowEddedder to voxelize Ipfo into the voxel space and seatter Vio with the voxelization indexes of Ip into the corresponding voxel cell, resulting in dense flow representation VEEV which spatially aligns with camera and LiDAR BEV features. V is used to supervise the predicted flow Vlg flow
The core changes compared with
A.9 ALGORITHM TO GENERATE DENSE FLOW REPRESENTATION
Algorithm summarizes the steps to generate a dense ground truth flow representation from bound- ing boxes, which serves supervision for A-BEVFlow.
Algorithm 1 Generation of dense ground truth flow representation
ego motion ' standard Require: Bounding boxes at time fo. B/o; Bounding boxes 1, ego
grid BEV
1: for each box 6/ ~ Bío do
2: for each box b ~ B do
Boxes are defined in the global coordinate.
3:
if b. .ID = b ID then
4:
Tei
hon2g + bt .ennee,, b oorientation
5:
Tho boa2g b center, b. orientation
6:
The Teg + ot
7:
Th
bow21o
)-1 (T 12c)
(Te2g)
T
bor2g
> Transfer the box at t1 to LiDAR frame
to
8:
TAo
boarto
)- (T"2g)) )-
T hox2g
Transfer the box at lo LiDAR frame
ln
9:
(ooo2t )-1 F. + [nr in bomes (b% G
Reg21,
+Th 2ooe2
T
>> Calculate the motion of boxes from to to t
10:
b Get grid points in the box at to P Po50 & Calculate the scene flow for the points within box
ytt + Rea211 P,500 6,0
11:
13:
end if
14:
end for
15:
end for
16: return woo { (yto-th
8:00 e BB..
J. Ppo + ,,, e B''}
A. THE USE OF LARGE LANGUAGE MODELS (LLMs)
Large Language Models (LLMs) used only polish the grammar and phrasing of sentences and to search for potentially relevant prior works. All conceptual development, experimental design, implementation, analysis, and initial writing of the paper were carried out by the authors.
17


Reviews:
[{"review_id": "gFV4IJPlcp", "reviewer": "Reviewer_VrJt", "summary": "This paper introduces AsyncBEV, a module designed to improve the robustness of LiDAR–camera 3D object detection under asynchrony. The proposed module predicts a Δ-BEVFlow, a BEV flow to spatially align asynchronous lidar features prior to fusion. The approach generalizes across both grid and token-based BEV detectors and improves performance over ego-motion compensation on nuScenes, particularly for dynamic objects and large temporal offsets (0.1–0.5 s). While technically sound and empirically validated, the work would benefit from stronger comparison against alternative approaches to help motivate the usefulness.", "questions": "- How does AsyncBEV compare to fallback strategies such as camera-only inference or simple temporal propagation when LiDAR is delayed or missing?\n- What are the performance and latency trade-offs relative to recent streaming or time-aware fusion methods such as StreamingFlow?\n- Could Δ-BEVFlow be trained to handle uncertain or estimated time offsets instead of relying on ground-truth Δt?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Conceptually intuitive and interpretable formulation for feature alignment under temporal misalignment and dynamic motion.\n- Generalizable design compatible with both grid and token BEV frameworks.\n- Consistent improvements across offset magnitudes with negligible runtime overhead.", "weaknesses": "- Lacks discussion of real-world latency handling in AV stacks, where stale data (>100 ms) are typically discarded. Comparison to such baselines (e.g., camera-only inference or temporal propagation) would clarify practical value.\n- Limited benchmarking against contemporary temporal alignment methods like StreamingFlow.\n- Performance could be impacted by stochastic latency profiles, but no analysis is provided on sensitivity to time offset estimation error.", "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": "", "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": "0b1Qc5k519", "reviewer": "Reviewer_LiTB", "summary": "This paper addresses a critical challenge in autonomous driving perception: sensor asynchrony (caused by mismatched sensor frequencies, network latency, or hardware bottlenecks). Most 3D object detectors rely on perfectly synchronized multi-modal data (LiDAR/camera), but asynchrony degrades performance—especially for dynamic objects, which are key to safety. To solve this, the authors propose AsyncBEV, a lightweight, generic module compatible with both token-based (e.g., CMT) and grid-based (e.g., UniBEV) detectors.\nAsyncBEV introduces Δ-BEVFlow estimation, a novel task that predicts 2D flow in Bird’s Eye View (BEV) space between multi-modal features using known time offsets. Unlike Ego Motion Compensation (EMC, which only aligns static objects) or traditional scene flow methods (requiring point clouds and fixed offsets), Δ-BEVFlow explicitly models dynamic object motion. It offers two formulations: motion-based (direct flow regression) and velocity-based (predict velocity first, then scale by time)—the latter is adopted for better regularization.\nExperiments on the nuScenes dataset show AsyncBEV significantly enhances robustness: in the worst-case 0.5s time offset, it outperforms EMC baselines by 16.6% (CMT) and 11.9% (UniBEV) in NDS for dynamic objects, with minimal computational overhead (marginal FPS loss).", "questions": "**Limited Novelty Compared to Prior Asynchrony-Robust Work**\n\nThe core idea of using BEV flow for asynchrony compensation is not entirely new. For example, CoBEVFlow (Wei et al., NeurIPS 2023) already uses BEV flow to handle asynchronous collaborative perception, though it relies on object proposals and is more computationally heavy. Additionally, recent work like UniV2X (Yu et al., AAAI 2025) explores end-to-end autonomous driving with V2X cooperation, which also involves addressing multi-agent asynchrony. The paper acknowledges these works but does not sufficiently emphasize how Δ-BEVFlow advances beyond them—e.g., why feature-based flow (without proposals) offers better generalization, or how variable time offset handling outperforms alternatives. This weakens the claim of novelty.\n\n**Simplified Asynchrony Assumptions**\n\nThe paper assumes only two sensors (one synchronous reference, one asynchronous), but real autonomous driving systems use 5–10 sensors (e.g., 6 cameras, 1 LiDAR, 5 radars in nuScenes). AsyncBEV cannot handle scenarios where 3+ sensors have overlapping offsets (e.g., LiDAR delayed by 0.2s, front camera by 0.1s). The authors mention extending to multi-sensor asynchrony as future work, but the current design lacks scalability to full-scale vehicle perception pipelines.\n\n**Synchronous Performance Trade-Off**\n\nDefault AsyncBEV causes small but consistent performance drops in the synchronized case (0s offset): 0.4% NDS for CMT and 1.0% NDS for UniBEV (Table 1). The \"frozen detector\" variant (AsyncBEV-FD) avoids this drop but underperforms in asynchronous scenarios. The paper does not explore alternative training strategies (e.g., adaptive loss weighting) to resolve this trade-off—critical for deployment, as synchronized sensors are the most common real-world scenario.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**High Practical Relevance**\n\nSensor asynchrony is unavoidable in real-world autonomous driving, yet it is often overlooked in detector design. By targeting this gap, AsyncBEV directly improves the safety and reliability of perception systems—particularly for dynamic objects (e.g., pedestrians, moving vehicles), which are the primary cause of accidents. This makes the work valuable for both academic research and industrial deployment.\n\n**Effective Δ-BEVFlow Design** \n\nΔ-BEVFlow addresses key limitations of prior methods: it operates on multi-modal BEV features (not raw point clouds) and supports variable time offsets, enabling flexible cross-modal alignment. The velocity-based formulation further strengthens the design: by decoupling velocity from time, it ensures flow approaches zero for near-synchronous sensors (avoiding unnecessary distortions) and simplifies learning, as validated by ablation studies (Table 2).\n\n**Generality and Lightweight Integration**\n\nAsyncBEV is architecture-agnostic: it adapts to token-based detectors (by adjusting token coordinates) and grid-based detectors (by generating grid look-up tables) with minimal modifications. It also introduces negligible computational overhead—experiments show only a 0.3–0.4 FPS drop (Table 1)—making it suitable for real-time autonomous driving pipelines.\n\n**Strong Interpretability**\n\nExplicit flow prediction allows intuitive visualization (Figures 5, 6), where predicted Δ-BEVFlow closely aligns with ground truth and directly corrects bounding box misalignments. Quantitative results (e.g., Figure 4’s flat performance curves for AsyncBEV) further confirm its robustness across varying time offsets, enhancing trust in the module’s mechanism.", "weaknesses": "**Limited Novelty Compared to Prior Asynchrony-Robust Work**\n\nThe core idea of using BEV flow for asynchrony compensation is not entirely new. For example, CoBEVFlow (Wei et al., NeurIPS 2023) already uses BEV flow to handle asynchronous collaborative perception, though it relies on object proposals and is more computationally heavy. Additionally, recent work like UniV2X (Yu et al., AAAI 2025) explores end-to-end autonomous driving with V2X cooperation, which also involves addressing multi-agent asynchrony. The paper acknowledges these works but does not sufficiently emphasize how Δ-BEVFlow advances beyond them—e.g., why feature-based flow (without proposals) offers better generalization, or how variable time offset handling outperforms alternatives. This weakens the claim of novelty.\n\n**Simplified Asynchrony Assumptions**\n\nThe paper assumes only two sensors (one synchronous reference, one asynchronous), but real autonomous driving systems use 5–10 sensors (e.g., 6 cameras, 1 LiDAR, 5 radars in nuScenes). AsyncBEV cannot handle scenarios where 3+ sensors have overlapping offsets (e.g., LiDAR delayed by 0.2s, front camera by 0.1s). The authors mention extending to multi-sensor asynchrony as future work, but the current design lacks scalability to full-scale vehicle perception pipelines.\n\n**Synchronous Performance Trade-Off**\n\nDefault AsyncBEV causes small but consistent performance drops in the synchronized case (0s offset): 0.4% NDS for CMT and 1.0% NDS for UniBEV (Table 1). The \"frozen detector\" variant (AsyncBEV-FD) avoids this drop but underperforms in asynchronous scenarios. The paper does not explore alternative training strategies (e.g., adaptive loss weighting) to resolve this trade-off—critical for deployment, as synchronized sensors are the most common real-world scenario.", "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": "", "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": "xI77p2xmkK", "reviewer": "Reviewer_zA9G", "summary": "This work presents a novel framework for improving the robustness of 3D object detection models in the presence of asynchronous sensors. The proposed framework consists of AsyncBEV, a lightweight and generic module that can be integrated into existing BEV object detection models for improved robustness against sensor asynchrony. AsyncBEV estimates the 2D flow in the BEV feature space and then warps the asynchronous sensor data to align with a reference frame. Extensive experiments on the nuScenes dataset with both grid-based and token-based BEV detectors show the effectiveness of the proposed approach in varying levels of asynchrony between LiDAR and camera sensors, especially for dynamic objects.", "questions": "The paper is well written, and the claims are validated in the experiments. My main concern is regarding simpler alternatives as baselines to better contextualize the benefits of the delta flow formulation (more details in the weaknesses above):\n- A finetuning variant for both CMT & UniBEV should be added to Tab.1 since these methods are not trained on asynchronous data.\n- A finetuning variant where the delta timestep offset is used as additional input should also be considered.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Addressing robustness against sensor asynchrony is important from a practical viewpoint since autonomous vehicles often come equipped with multiple sensors.\n- The paper is well-written and easy to follow. The description is detailed and the figures (Fig.2,3,5) are informative.\n- AsyncBEV module is a generic and lightweight module that can be combined with both grid-based and token-based BEV detectors.\n- The proposed module predicts the delta 2D scene flow in BEV space, conditioned on delta timesteps across multimodal BEV features.\n- Experiments on nuScenes (Tab.1) show the benefits of AsyncBEV over egomotion compensation (EMC) on both token-based (CMT) and grid-based (UniBEV) approaches.\n- Ablations in Fig.4, Tab.2 provide more insights into the capabilities of different components.", "weaknesses": "- In Sec.4.2, for the finetuning UniBEV variant, is the delta timestep offset (between reference and asynchronous sensor) also used as input? It'd be useful to have a finetuning baseline that also incorporates delta timestep offset. For example, LiDAR BEV can be augmented with the delta timestep (on a per-point basis) as an additional feature channel (a similar strategy was also used in the Fan et al. 2025 referenced paper). This would help understand if the delta flow formulation is indeed effective compared to simpler alternatives like finetuning with the delta timestep as an additional feature channel.\n- The finetuning variant in Tab.2 should also be a baseline in Tab.1 (applied to both CMT and UniBEV) since it leads to extensive gains (as noted in Tab.2). This is relevant since CMT and UniBEV are not trained with any asynchronous data.\n- Since EMC is quite widely used in the autonomous driving literature (as mentioned in the paper), it'd be useful to report the delta gains with respect to EMC variants in Tab.1. The gains of the proposed approach are still clear, this would better contextualize the benefits of AsyncBEV over the standard EMC approach.", "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": "", "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": "KjxAzqLWim", "reviewer": "Reviewer_gWRm", "summary": "This paper proposes AsyncBEV, a lightweight module designed to enhance the robustness of multi-modal 3D object detectors against sensor asynchrony by introducing a novel Δ-BEVFlow estimation task. The method predicts motion in BEV feature space and warps asynchronous features to a reference timestamp, demonstrating compatibility with both token-based and grid-based detectors. The writing is clear and well-structured, and the experimental evaluation on nuScenes shows significant improvements in handling asynchronous inputs, especially for dynamic objects.", "questions": "1.Have the authors considered evaluating AsyncBEV on other autonomous driving datasets, such as Waymo, to further demonstrate its generalization capability?\n\n2.How would the method scale and perform when more than two sensors are asynchronous simultaneously, and would the current flow estimation strategy still be effective?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The paper is clearly written and easy to follow.\n\n2.It explores an important and underexplored problem in multi-modal perception.\n\n3.The method is simple yet effective, with a lightweight and generic design.\n\n4.Extensive experiments validate the effectiveness of the proposed approach under various asynchronous settings.", "weaknesses": "1.The validation is limited to the nuScenes dataset, lacking cross-dataset generalization.\n\n2.The method does not address scenarios where multiple sensors are asynchronous at the same time, which may lead to increased computational complexity.\n\n3.The Motion Compensation-based baseline is somewhat weak. The comparison to asynchronous fusion methods in related work, like StreamingFlow and TimeAlign, is missing.", "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": "", "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:

Decision: Accept (Poster)