1st SSLAD Track 2 - 3D Object Detection
This challenge is a part of ICCV2021 workshop "Self-supervised Learning for Next-Generation Industry-level Autonomous Driving". The aim of this track is to utilize both labeled data and unlabled data to achieve industry-level autonomous driving solutions
For 3D object detection, we provide a large-scale dataset with 1 million point clouds and 7 million images. We annotated 5K, 3K and 8K scenes for training, validation and testing set respectively and leave the other scenes unlabeled. We provide 3D bounding boxes for car, cyclist, pedestrian, truck and bus.
Currently 3D Detection challenge has been released at CodaLab