Figure
Source publication
In this paper, a structural-output is obtained to estimate 3D human pose using 3D human point cloud and monocular
images. The Neural Network takes a human image and 3D pose as inputs and gives outputs a score value. Conditional Random
Field (CRF) approach is using to semantically classify human limbs in its point cloud for 3D human pose production....
Context in source publication
Citations
... Experiments demonstrate that it performs well in a variety of 3D tasks, including object categorization and semantic segmentation. In [12], conditional random fields are used to classify 3D human limbs in point clouds. ...
... Biswas et al. [64] designed an end-to-end system that combines RGB images and point cloud information to recover 3D human pose. Özbay et al. [65] used a simplified extraction method "Conditional Random Field" to classify 3D human point clouds, and the corresponding images and poses as input of CNN transmitted similar spaces. When the image-pose pair is matched, the value of dot product is high, otherwise the value is low. ...
Joint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.
