TY - GEN

T1 - StreamLTS

T2 - 18th European Conference on Computer Vision, ECCV 2024

AU - Yuan, Yunshuang

AU - Sester, Monika

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Cooperative perception via communication among intelligent traffic agents has great potential to improve the safety of autonomous driving. However, limited communication bandwidth, localization errors and asynchronized capturing time of sensor data, all introduce difficulties to the data fusion of different agents. To some extend, previous works have attempted to reduce the shared data size, mitigate the spatial feature misalignment caused by localization errors and communication delay. However, none of them have considered the asynchronized sensor ticking times, which can lead to dynamic object misplacement of more than one meter during data fusion. In this work, we propose Time-Aligned COoperative Object Detection (TA-COOD), for which we adapt widely used dataset OPV2V and DairV2X with considering asynchronous LiDAR sensor ticking times and build an efficient fully sparse framework with modeling the temporal information of individual objects with query-based techniques. The experiment results confirmed the superior efficiency of our fully sparse framework compared to the state-of-the-art dense models. More importantly, they show that the point-wise observation timestamps of the dynamic objects are crucial for accurate modeling the object temporal context and the predictability of their time-related locations. The official code is available at https://github.com/YuanYunshuang/CoSense3D.

AB - Cooperative perception via communication among intelligent traffic agents has great potential to improve the safety of autonomous driving. However, limited communication bandwidth, localization errors and asynchronized capturing time of sensor data, all introduce difficulties to the data fusion of different agents. To some extend, previous works have attempted to reduce the shared data size, mitigate the spatial feature misalignment caused by localization errors and communication delay. However, none of them have considered the asynchronized sensor ticking times, which can lead to dynamic object misplacement of more than one meter during data fusion. In this work, we propose Time-Aligned COoperative Object Detection (TA-COOD), for which we adapt widely used dataset OPV2V and DairV2X with considering asynchronous LiDAR sensor ticking times and build an efficient fully sparse framework with modeling the temporal information of individual objects with query-based techniques. The experiment results confirmed the superior efficiency of our fully sparse framework compared to the state-of-the-art dense models. More importantly, they show that the point-wise observation timestamps of the dynamic objects are crucial for accurate modeling the object temporal context and the predictability of their time-related locations. The official code is available at https://github.com/YuanYunshuang/CoSense3D.

KW - Cooperative Perception

KW - Data Fusion

KW - Point Cloud

UR - http://www.scopus.com/inward/record.url?scp=105006881323&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-91813-1_3

DO - 10.1007/978-3-031-91813-1_3

M3 - Conference contribution

AN - SCOPUS:105006881323

SN - 9783031918124

T3 - Lecture Notes in Computer Science

SP - 34

EP - 51

BT - Computer Vision – ECCV 2024 Workshops, Proceedings

A2 - Del Bue, Alessio

A2 - Canton, Cristian

A2 - Pont-Tuset, Jordi

A2 - Tommasi, Tatiana

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 29 September 2024 through 4 October 2024

ER -