AsyncBEV: Cross-modal Flow Alignment in Asynchronous 3D Object Detection

• URL: http://arxiv.org/abs/2601.12994v1
• Date: Mon, 19 Jan 2026 12:22:57 GMT
• Title: AsyncBEV: Cross-modal Flow Alignment in Asynchronous 3D Object Detection
• Authors: Shiming Wang, Holger Caesar, Liangliang Nan, Julian F. P. Kooij,
• Abstract summary: AsyncBEV improves robustness of 3D Birds' Eye View (BEV) object detection models against sensor asynchrony.<n>Inspired by scene flow estimation, AsyncBEV first estimates the 2D flow from the BEV features of two different sensor modalities.<n>We show AsyncBEV can easily be integrated into different current BEV detector architectures.
• Score: 24.862978565737947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Birds' 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 will be released upon acceptance.

Related papers

• OnlineBEV: Recurrent Temporal Fusion in Bird's Eye View Representations for Multi-Camera 3D Perception [13.143625047012604]
  Multi-view camera-based 3D perception can be conducted using bird's eye view (BEV) features obtained through perspective view-to-BEV transformations.<n>OnlineBEV combines BEV features over time using a recurrent structure.<n>OnlineBEV achieves 63.9% NDS on the nuScenes test set, recording state-of-the-art performance in the camera-only 3D object detection task.
  arXiv  Detail & Related papers  (2025-07-11T14:48:59Z)
• ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions [91.55655961014027]
  3D semantic occupancy and flow prediction are fundamental to understanding scene scene.<n>This paper proposes a vision-based framework with three targeted improvements.<n>Our purely convolutional architecture establishes new SOTA performance on multiple benchmarks for both semantic occupancy and joint semantic-flow prediction.
  arXiv  Detail & Related papers  (2024-11-12T11:32:56Z)
• Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow [45.670727141966545]
  Collaborative perception can boost each agent's perception ability by facilitating communication among multiple agents. However, temporal asynchrony among agents is inevitable in the real world due to communication delays, interruptions, and clock misalignments. We propose CoBEVFlow, an asynchrony-robust collaborative perception system based on bird's eye view (BEV) flow.
  arXiv  Detail & Related papers  (2023-09-29T02:45:56Z)
• SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos [20.51396212498941]
  SparseBEV is a fully sparse 3D object detector that outperforms the dense counterparts. On the test split of nuScenes, SparseBEV achieves the state-of-the-art performance of 67.5 NDS.
  arXiv  Detail & Related papers  (2023-08-18T02:11:01Z)
• OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection [29.530177591608297]
  Multi-view 3D object detection is becoming popular in autonomous driving due to its high effectiveness and low cost. Most of the current state-of-the-art detectors follow the query-based bird's-eye-view (BEV) paradigm. We propose an Object-Centric query-BEV detector OCBEV, which can carve the temporal and spatial cues of moving targets more effectively.
  arXiv  Detail & Related papers  (2023-06-02T17:59:48Z)
• Multi-Modal 3D Object Detection by Box Matching [109.43430123791684]
  We propose a novel Fusion network by Box Matching (FBMNet) for multi-modal 3D detection. With the learned assignments between 3D and 2D object proposals, the fusion for detection can be effectively performed by combing their ROI features.
  arXiv  Detail & Related papers  (2023-05-12T18:08:51Z)
• MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation [104.12419434114365]
  In real-world applications, sensor corruptions and failures lead to inferior performances. We propose a robust framework, called MetaBEV, to address extreme real-world environments. We show MetaBEV outperforms prior arts by a large margin on both full and corrupted modalities.
  arXiv  Detail & Related papers  (2023-04-19T16:37:17Z)
• BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation [105.96557764248846]
  We introduce BEVFusion, a generic multi-task multi-sensor fusion framework. It unifies multi-modal features in the shared bird's-eye view representation space. It achieves 1.3% higher mAP and NDS on 3D object detection and 13.6% higher mIoU on BEV map segmentation, with 1.9x lower cost.
  arXiv  Detail & Related papers  (2022-05-26T17:59:35Z)
• M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation [145.6041893646006]
  M$2$BEV is a unified framework that jointly performs 3D object detection and map segmentation. M$2$BEV infers both tasks with a unified model and improves efficiency.
  arXiv  Detail & Related papers  (2022-04-11T13:43:25Z)
• Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction [51.072733683919246]
  We introduce Recurrent Asynchronous Multimodal (RAM) networks to handle asynchronous and irregular data from multiple sensors. Inspired by traditional RNNs, RAM networks maintain a hidden state that is updated asynchronously and can be queried at any time to generate a prediction. We show an improvement over state-of-the-art methods by up to 30% in terms of mean depth absolute error.
  arXiv  Detail & Related papers  (2021-02-18T13:24:35Z)
• EBBINNOT: A Hardware Efficient Hybrid Event-Frame Tracker for Stationary Dynamic Vision Sensors [5.674895233111088]
  This paper presents a hybrid event-frame approach for detecting and tracking objects recorded by a stationary neuromorphic sensor. To exploit the background removal property of a static DVS, we propose an event-based binary image creation that signals presence or absence of events in a frame duration. This is the first time a stationary DVS based traffic monitoring solution is extensively compared to simultaneously recorded RGB frame-based methods.
  arXiv  Detail & Related papers  (2020-05-31T03:01:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.