TITLE: ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data 

ABSTRACT: Scene understanding is an active research area. Commercial depth sensors,
such as Kinect, have enabled the release of several RGB-D datasets over the
past few years which spawned novel methods in 3D scene understanding. More
recently with the launch of the LiDAR sensor in Apple's iPads and iPhones, high
quality RGB-D data is accessible to millions of people on a device they
commonly use. This opens a whole new era in scene understanding for the
Computer Vision community as well as app developers. The fundamental research
in scene understanding together with the advances in machine learning can now
impact people's everyday experiences. However, transforming these scene
understanding methods to real-world experiences requires additional innovation
and development. In this paper we introduce ARKitScenes. It is not only the
first RGB-D dataset that is captured with a now widely available depth sensor,
but to our best knowledge, it also is the largest indoor scene understanding
data released. In addition to the raw and processed data from the mobile
device, ARKitScenes includes high resolution depth maps captured using a
stationary laser scanner, as well as manually labeled 3D oriented bounding
boxes for a large taxonomy of furniture. We further analyze the usefulness of
the data for two downstream tasks: 3D object detection and color-guided depth
upsampling. We demonstrate that our dataset can help push the boundaries of
existing state-of-the-art methods and it introduces new challenges that better
represent real-world scenarios.
Scene understanding is an active research area. Commercial depth sensors,
such as Kinect, have enabled the release of several RGB-D datasets over the
past few years which spawned novel methods in 3D scene understanding. More
recently with the launch of the LiDAR sensor in Apple's iPads and iPhones, high
quality RGB-D data is accessible to millions of people on a device they
commonly use. This opens a whole new era in scene understanding for the
Computer Vision community as well as app developers. The fundamental research
in scene understanding together with the advances in machine learning can now
impact people's everyday experiences. However, transforming these scene
understanding methods to real-world experiences requires additional innovation
and development. In this paper we introduce ARKitScenes. It is not only the
first RGB-D dataset that is captured with a now widely available depth sensor,
but to our best knowledge, it also is the largest indoor scene understanding
data released. In addition to the raw and processed data from the mobile
device, ARKitScenes includes high resolution depth maps captured using a
stationary laser scanner, as well as manually labeled 3D oriented bounding
boxes for a large taxonomy of furniture. We further analyze the usefulness of
the data for two downstream tasks: 3D object detection and color-guided depth
upsampling. We demonstrate that our dataset can help push the boundaries of
existing state-of-the-art methods and it introduces new challenges that better
represent real-world scenarios.
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data
http://arxiv.org/abs/2111.08897v3