3 accepted papers
Self-supervised image backbones can be used to address complex 2D tasks (e.g. semantic segmentation object discovery) very efficiently and with little or no downstream supervision. Ideally 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D featu…
We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled, and to use the underlying latent vectors as…
Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in autonomous driving to allow a vehicle to act safely in its 3D environment. The best performing methods in 3D semantic segmentation or object detection rely on a large amount of annotated data. Yet annotating 3D L…