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Scene understanding is an active research area. Commercial depth sensors,such as Kinect, have enabled the release of several RGB-D datasets over thepast few years which spawned novel methods in 3D scene understanding. Morerecently with the launch of the LiDAR sensor in Apple's iPads and iPhones, highquality RGB-D data is accessible to millions of people on a device theycommonly use. This opens a whole new era in scene understanding for theComputer Vision community as well as app developers. The fundamental researchin scene understanding together with the advances in machine learning can nowimpact people's everyday experiences. However, transforming these sceneunderstanding methods to real-world experiences requires additional innovationand development. In this paper we introduce ARKitScenes. It is not only thefirst 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 understandingdata released. In addition to the raw and processed data from the mobiledevice, ARKitScenes includes high resolution depth maps captured using astationary laser scanner, as well as manually labeled 3D oriented boundingboxes for a large taxonomy of furniture. We further analyze the usefulness ofthe data for two downstream tasks: 3D object detection and color-guided depthupsampling. We demonstrate that our dataset can help push the boundaries ofexisting state-of-the-art methods and it introduces new challenges that betterrepresent real-world scenarios.

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