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

For the last few decades, several major subfields of artificial intelligence including computer vision, graphics, and robotics have progressed largely independently from each other. Moreover, we established benchmarks and baselines for several tasks relevant to mobile perception, encompassing problems from computer vision, graphics, and robotics on the same dataset, e.g., semantic scene understanding, novel view synthesis and semantic SLAM.

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• 1 Recently, however, the community has realized that progress towards robust intelligent systems such as self-driving cars requires a concerted effort across the different fields.
• 2 This motivated us to develop KITTI-360, successor of the popular KITTI dataset.
• 3 KITTI-360 is a suburban driving dataset which comprises richer input modalities, comprehensive semantic instance annotations and accurate localization to facilitate research at the intersection of vision, graphics and robotics.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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