Ev-Layout：一个用于室内布局估计与跟踪的大规模事件驱动多模态数据集
Ev-Layout: A Large-scale Event-based Multi-modal Dataset for Indoor Layout Estimation and Tracking
AI总结：
本文提出Ev-Layout大规模事件驱动多模态数据集，并设计融合事件-时间分布特征与时空特征的事件布局估计流程，显著提升动态室内布局估计精度。
摘要：
本文提出了Ev-Layout，一个新颖的大规模事件驱动多模态数据集，旨在用于室内布局估计与跟踪。Ev-Layout为研究社区做出了关键贡献：利用一个混合数据采集平台（配备头戴式显示器和VR界面），该平台集成了RGB相机和仿生事件相机，以捕捉运动状态下的室内布局。数据集纳入了数据采集过程中记录的惯性测量单元（IMUs）时间序列数据和环境光照条件，以突出运动速度和光照对布局估计精度的潜在影响。该数据集包含2.5K个序列，包括超过771.3K张RGB图像和100亿个事件数据点。其中，39K张图像带有室内布局标注，可支持基于事件和基于视频的室内布局估计研究。基于该数据集，我们提出了一种事件驱动的布局估计流程，其中包含一个新颖的事件-时间分布特征模块，以有效聚合来自事件数据的时空信息。此外，我们引入了一个时空特征融合模块，该模块可以方便地集成到transformer模块中用于融合。最后，我们在Ev-Layout数据集上进行了基准测试和大量实验，结果表明，与现有基于事件的方法相比，我们的方法显著提高了动态室内布局估计的精度。
英文摘要：
This paper presents Ev-Layout, a novel large-scale event-based multi-modal dataset designed for indoor layout estimation and tracking. Ev-Layout makes key contributions to the community by: Utilizing a hybrid data collection platform (with a head-mounted display and VR interface) that integrates both RGB and bio-inspired event cameras to capture indoor layouts in motion. Incorporating time-series data from inertial measurement units (IMUs) and ambient lighting conditions recorded during data collection to highlight the potential impact of motion speed and lighting on layout estimation accuracy. The dataset consists of 2.5K sequences, including over 771.3K RGB images and 10 billion event data points. Of these, 39K images are annotated with indoor layouts, enabling research in both event-based and video-based indoor layout estimation. Based on the dataset, we propose an event-based layout estimation pipeline with a novel event-temporal distribution feature module to effectively aggregate the spatio-temporal information from events. Additionally, we introduce a spatio-temporal feature fusion module that can be easily integrated into a transformer module for fusion purposes. Finally, we conduct benchmarking and extensive experiments on the Ev-Layout dataset, demonstrating that our approach significantly improves the accuracy of dynamic indoor layout estimation compared to existing event-based methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
