Scene graphs of point clouds help to understand object-level relationships in the 3D space. Most graph generation methods work on 2D structured data, which cannot be used for the 3D unstructured point cloud data. Existing point-cloud-based methods generate the scene graph with an additional graph structure that needs labor-intensive manual annotation. To address these problems, we explore a method to convert the point clouds into structured data and generate graphs without given structures. Specifically, we cluster points with similar augmented features into groups and establish their relationships, resulting in an initial structural representation of the point cloud. Besides, we propose a Dynamic Graph Generation Network (DGGN) to judge the semantic labels of targets of different granularity. It dynamically splits and merges point groups, resulting in a scene graph with high precision. Experiments show that our methods outperform other baseline methods. They output reliable graphs describing the object-level relationships without additional manual labeled data.

Human pose estimation has received significant attention recently due to its various applications in the real world. As the performance of the state-of-the-art human pose estimation methods can be improved by deep learning, this paper presents a comprehensive survey of deep learning based human pose estimation methods and analyzes the methodologies employed. We summarize and discuss recent works with a methodology-based taxonomy. Single-person and multi-person pipelines are first reviewed separately. Then, the deep learning techniques applied in these pipelines are compared and analyzed. The datasets and metrics used in this task are also discussed and compared. The aim of this survey is to make every step in the estimation pipelines interpretable and to provide readers a readily comprehensible explanation. Moreover, the unsolved problems and challenges for future research are discussed.