Abstract: The present invention belongs to the technical field of 3D reconstruction in the field of computer vision, and provides a dataset generation method for self-supervised learning scene point cloud completion based on panoramas. Pairs of incomplete point cloud and target point cloud with RGB information and normal information can be generated by taking RGB panoramas, depth panoramas and normal panoramas in the same view as input for constructing a self-supervised learning dataset for training of the scene point cloud completion network. The key points of the present invention are occlusion prediction and equirectangular projection based on view conversion, and processing of the stripe problem and point-to-point occlusion problem during conversion. The method of the present invention includes simplification of the collection mode of the point cloud data in a real scene; occlusion prediction idea of view conversion; and design of view selection strategy.

Abstract: The present invention belongs to the technical field of 3D reconstruction in the field of computer vision, and provides a dataset generation method for self-supervised learning scene point cloud completion based on panoramas. Pairs of incomplete point cloud and target point cloud with RGB information and normal information can be generated by taking RGB panoramas, depth panoramas and normal panoramas in the same view as input for constructing a self-supervised learning dataset for training of the scene point cloud completion network. The key points of the present invention are occlusion prediction and equirectangular projection based on view conversion, and processing of the stripe problem and point-to-point occlusion problem during conversion. The method of the present invention includes simplification of the collection mode of the point cloud data in a real scene; occlusion prediction idea of view conversion; and design of view selection strategy.

Abstract: The present invention belongs to the technical field of computer vision and provides a data active selection method for robot grasping. The core content of the present invention is a data selection strategy module, which shares the feature extraction layer of backbone main network and integrates the features of three receptive fields with different sizes. While making full use of the feature extraction module, the present invention greatly reduces the amount of parameters that need to be added. During the training process of the main grasp method detection network model, the data selection strategy module can be synchronously trained to form an end-to-end model. The present invention makes use of naturally existing labeled and unlabeled labels, and makes full use of the labeled data and the unlabeled data. When the amount of the labeled data is small, the network can still be more fully trained.