Details
| Original language | English |
|---|---|
| Title of host publication | Computer Vision – ECCV 2024 Workshops, Proceedings |
| Editors | Alessio Del Bue, Cristian Canton, Jordi Pont-Tuset, Tatiana Tommasi |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 34-51 |
| Number of pages | 18 |
| ISBN (print) | 9783031918124 |
| Publication status | Published - 2025 |
| Event | 18th European Conference on Computer Vision, ECCV 2024 - Milan, Italy Duration: 29 Sept 2024 → 4 Oct 2024 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Volume | 15630 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
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.
Keywords
- Cooperative Perception, Data Fusion, Point Cloud
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Computer Vision – ECCV 2024 Workshops, Proceedings. ed. / Alessio Del Bue; Cristian Canton; Jordi Pont-Tuset; Tatiana Tommasi. Springer Science and Business Media Deutschland GmbH, 2025. p. 34-51 (Lecture Notes in Computer Science; Vol. 15630 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
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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 -