|
|
|
|
| LEADER |
01000naa a22002652c 4500 |
| 001 |
NLM393153479 |
| 003 |
DE-627 |
| 005 |
20250927232314.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
250927s2025 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2025.3615144
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n1582.xml
|
| 035 |
|
|
|a (DE-627)NLM393153479
|
| 035 |
|
|
|a (NLM)41004360
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Luo, Xinglong
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Learning Efficient Meshflow and Optical Flow from Event Cameras
|
| 264 |
|
1 |
|c 2025
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 26.09.2025
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status Publisher
|
| 520 |
|
|
|a In this paper, we explore the problem of event-based meshflow estimation, a novel task that involves predicting a spatially smooth sparse motion field from event cameras. To start, we review the state-of-the-art in event-based flow estimation, highlighting two key areas for further research: i) the lack of meshflow-specific event datasets and methods, and ii) the underexplored challenge of event data density. First, we generate a large-scale High-Resolution Event Meshflow (HREM) dataset, which showcases its superiority by encompassing the merits of high resolution at 1280×720, handling dynamic objects and complex motion patterns, and offering both optical flow and meshflow labels. These aspects have not been fully explored in previous works. Besides, we propose Efficient Event-based MeshFlow (EEMFlow) network, a lightweight model featuring a specially crafted encoder-decoder architecture to facilitate swift and accurate meshflow estimation. Furthermore, we upgrade EEMFlow network to support dense event optical flow, in which a Confidence-induced Detail Completion (CDC) module is proposed to preserve sharp motion boundaries. We conduct comprehensive experiments to show the exceptional performance and runtime efficiency (30× faster) of our EEMFlow model compared to the recent state-of-the-art flow method. As an extension, we expand HREM into HREM+, a multi-density event dataset contributing to a thorough study of the robustness of existing methods across data with varying densities, and propose an Adaptive Density Module (ADM) to adjust the density of input event data to a more optimal range, enhancing the model's generalization ability. We empirically demonstrate that ADM helps to significantly improve the performance of EEMFlow and EEMFlow+ by 8% and 10%, respectively
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Luo, Ao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Luo, Kunming
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Zhengning
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Tan, Ping
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zeng, Bing
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liu, Shuaicheng
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g PP(2025) vom: 26. Sept.
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
|
| 773 |
1 |
8 |
|g volume:PP
|g year:2025
|g day:26
|g month:09
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2025.3615144
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d PP
|j 2025
|b 26
|c 09
|