DDAD is a new autonomous driving benchmark from TRI (Toyota Research Institute) for long range (up to 250m) and dense depth estimation in challenging and diverse urban conditions. It contains monocular videos and accurate ground-truth depth (across a full 360 degree field of view) generated from high-density LiDARs mounted on a fleet of self-driving cars operating in a cross-continental setting. DDAD contains scenes from urban settings in the United States (San Francisco, Bay Area, Cambridge, Detroit, Ann Arbor) and Japan (Tokyo, Odaiba).
57 PAPERS • 1 BENCHMARK
The MannequinChallenge Dataset (MQC) provides in-the-wild videos of people in static poses while a hand-held camera pans around the scene. The dataset consists of three splits for training, validation and testing.
26 PAPERS • NO BENCHMARKS YET
The Web Stereo Video Dataset consists of 553 stereoscopic videos from YouTube. This dataset has a wide variety of scene types, and features many nonrigid objects.
12 PAPERS • NO BENCHMARKS YET