Abstract: A method and system for clustering-based panoptic segmentation of point clouds and a method of training the same are provided. Features of a point cloud that includes a plurality of points are extracted. Clusters of the plurality of points corresponding to objects from the features of the point cloud frame are identified. A subset of the plurality of points is selectively shifted using the features and the clusters of the plurality of points via a neural network that is trained to recognize a subset of points of objects that are closer to points of other objects than a distance between centroids of the corresponding objects and shift the subset of points away from the other objects.

Abstract: Methods and systems for computing a surface normal vector for a point cloud are disclosed. A sparse range map is generated by projecting the point cloud. A vertical kernel and a horizontal kernel are used to generate a vertical kernel range map and a horizontal kernel range map from the sparse range map. To complete an empty entry in the sparse range map, a guidance range map is used compute a vertical gradient and a horizontal gradient. The empty entry is filled using a corresponding entry in the vertical kernel range map or in the horizontal kernel range map, based on a comparison between the vertical gradient and the horizontal gradient. A surface normal vector is computed using the completed dense range map.