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For the last few decades, several major subfields of artificial intelligenceincluding computer vision, graphics, and robotics have progressed largelyindependently from each other. Recently, however, the community has realizedthat progress towards robust intelligent systems such as self-driving carsrequires a concerted effort across the different fields. This motivated us todevelop KITTI-360, successor of the popular KITTI dataset. KITTI-360 is asuburban driving dataset which comprises richer input modalities, comprehensivesemantic instance annotations and accurate localization to facilitate researchat the intersection of vision, graphics and robotics. For efficient annotation,we created a tool to label 3D scenes with bounding primitives and developed amodel that transfers this information into the 2D image domain, resulting inover 150k images and 1B 3D points with coherent semantic instance annotationsacross 2D and 3D. Moreover, we established benchmarks and baselines for severaltasks relevant to mobile perception, encompassing problems from computervision, graphics, and robotics on the same dataset, e.g., semantic sceneunderstanding, novel view synthesis and semantic SLAM. KITTI-360 will enableprogress at the intersection of these research areas and thus contributetowards solving one of today's grand challenges: the development of fullyautonomous self-driving systems.

KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D