The situation with lidars is more difficult because there was previously only one relevant dataset available: the KITTI dataset. To get started, Yandex developers had to label new data from scratch, and at this point Toloka came to the rescue again.
Labeling a point cloud is not a trivial procedure. Toloka users are not experts in 3D labeling, so it is a challenge to explain how 3D projections work and how they can identify cars in a point cloud, but a good lidar data flow was established.
The next challenge is to take the point clouds with 3D coordinates from around a car and neural networks that are optimized for detection, and feed the point clouds to the neural network as input. Yandex experimented with an approach in which projections are constructed with a birds-eye view of the points and then divided into cells. If there is at least one point in a given cell, it is considered to be occupied.