Accurate, up-to-date High-Definition (HD) maps are critical
for urban planning, infrastructure monitoring, and
autonomous navigation. However, these maps quickly become
outdated as environments evolve, creating a need for robust
methods that not only detect changes but also incorporate
them into updated 3D representations. While change detection
techniques have advanced significantly, there remains a
clear gap between detecting changes and actually updating 3D
maps, particularly when relying on 2D image-based change
detection. To address this gap, we introduce SceneEdited,
the first city-scale dataset explicitly designed to support
research on HD map maintenance through 3D point cloud
updating. SceneEdited contains over 800 up-to-date scenes
covering 73 km of driving and approximate 3 km^2 of urban
area, with more than 23,000 synthesized object changes
created both manually and automatically across 2000+
out-of-date versions, simulating realistic urban
modifications such as missing roadside infrastructure,
buildings, overpasses, and utility poles. Each scene
includes calibrated RGB images, LiDAR scans, and detailed
change masks for training and evaluation. We also provide
baseline methods using a foundational image-based
structure-from-motion pipeline for updating outdated scenes,
as well as a comprehensive toolkit supporting scalability,
trackability, and portability for future dataset expansion
and unification of out-of-date object annotations. Both the
dataset and the toolkit are publicly available at
https://github.com/ChadLin9596/ScenePoint-ETK, establising a
standardized benchmark for 3D map updating research.
