Ev-Layout: A Large-scale Event-based Multi-modal Dataset for Indoor Layout Estimation and Tracking
Journal:
arXiv
Published Date:
Mar 11, 2025
Abstract
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.
