GOOSE 3D Semantic Segmentation Challenge Label Data

This dataset consists of semantically segmented LiDAR point clouds of the GOOSE and GOOSE-Ex dataset. The original point clouds annotations segmented all points into 64 semantic classes, but for the GOOSE 3D Semantic Segmentation Challenge on CodaBench we consolidated the point cloud data into 8 superclasses (+ other class): category_name,label_key,hex other,0,#A9A9A9 artificial_structures,1,#DE88DE artificial_ground,2,#EBFF3B natural_ground,3,#A1887F obstacle,4,#FFC107 vehicle,5,#F44336 vegetation,6,#4CAF50 human,7,#8FB0FF sky,8,#2196F3 The dataset contains 13006 annotated point clouds in total, stored in the .label format as is done in the SemanticKITTI dataset. import numpy as np # reading a .label file label = np.fromfile(filename, dtype=np.uint32) label = label.reshape((-1))# extract the semantic and instance label IDs sem_label = label & 0xFFFF # semantic label in lower half inst_label = label >> 16 # instance id in upper half This dataset only contains the annotations, to download the LiDAR point cloud data, please visit the download dataset page in the GOOSE dataset documentation.