Abstract: Methods and systems for performing semantic scene completion of sparse 3D data are described. A frame of sparse 3D data is preprocessed into a sparse 3D tensor and a sparse 2D tensor. A partially completed 3D tensor is generated from the sparse 3D tensor using a 3D prediction network, and a semantically completed 2D tensor is generated from the sparse 2D tensor using a 2D prediction network. The partially completed 3D tensor is completed to obtain a semantically completed 3D tensor by assigning a given class label, which has been assigned to a given pixel in the semantically completed 2D tensor, to a voxel at a corresponding x-y coordinate in the partially completed 3D tensor.

Abstract: System and method for semantic segmentation of point clouds. The method may include: generating, via a first neural network, a birds-eye-view (BEV) image of the environment from the aggregated point cloud; generating, via a second neural network, a labelled BEV image from the BEV image, wherein each pixel in the labelled BEV image is associated with a class label from a set of class labels; generating a BEV feature map; and generating, via a third neural network, the road surface segmentation map in the form of a refined labelled BEV image based on the labelled BEV image by smoothing the labelled BEV image using the BEV feature map, wherein each pixel in the refined labelled BEV image is associated with a class label from the set of class labels.

Abstract: System and method for semantic segmentation of point clouds. The method may include: generating, via a first neural network, a birds-eye-view (BEV) image of the environment from the aggregated point cloud; generating, via a second neural network, a labelled BEV image from the BEV image, wherein each pixel in the labelled BEV image is associated with a class label from a set of class labels; generating a BEV feature map; and generating, via a third neural network, the road surface segmentation map in the form of a refined labelled BEV image based on the labelled BEV image by smoothing the labelled BEV image using the BEV feature map, wherein each pixel in the refined labelled BEV image is associated with a class label from the set of class labels.

Abstract: Methods and systems for performing semantic scene completion of sparse 3D data are described. A frame of sparse 3D data is preprocessed into a sparse 3D tensor and a sparse 2D tensor. A partially completed 3D tensor is generated from the sparse 3D tensor using a 3D prediction network, and a semantically completed 2D tensor is generated from the sparse 2D tensor using a 2D prediction network. The partially completed 3D tensor is completed to obtain a semantically completed 3D tensor by assigning a given class label, which has been assigned to a given pixel in the semantically completed 2D tensor, to a voxel at a corresponding x-y coordinate in the partially completed 3D tensor.