Abstract: The invention discloses a method for three-dimensional human pose estimation, which can realize the real-time and high-precision 3D human pose estimation without high configuration hardware support and precise human body model. In this method for three-dimensional human pose estimation, including the following steps: (1) establishing a three-dimensional human body model matching the object, which is a cloud point human body model of visible spherical distribution constraint. (2) Matching and optimizing between human body model for human body pose tracking and depth point cloud. (3) Recovering for pose tracking error based on dynamic database retrieval.

Abstract: A combined remediation method of petroleum-contaminated soil includes: impurity removal pretreatment, photocatalytic pre-oxidation, stepwise thermal desorption of petroleum from soil, and high-temperature oxidation; with iron-titanium composite metal oxide (ITCMO) as a catalyst, conducting oxidation pretreatment under light conditions so that some cross-linked structures in macromolecular petroleum contaminants are broken and degraded; and conducting stepwise pyrolysis to achieve a removal rate of more than 98.00%. The new method adopts a combined remediation technology of photocatalytic pre-oxidation-stepwise pyrolysis, which realizes a relatively-high removal rate of petroleum hydrocarbons and the efficient and harmless remediation of high-concentration petroleum-contaminated soil, and remedied soil can be reused.

Abstract: The invention discloses a method for three-dimensional human pose estimation, which can realize the real-time and high-precision 3D human pose estimation without high configuration hardware support and precise human body model. In this method for three-dimensional human pose estimation, including the following steps: (1) establishing a three-dimensional human body model matching the object, which is a cloud point human body model of visible spherical distribution constraint. (2) Matching and optimizing between human body model for human body pose tracking and depth point cloud. (3) Recovering for pose tracking error based on dynamic database retrieval.

Abstract: 3D reconstruction method based on deep learning includes the following steps: (1) The potential vector constrained in the input image is used to reconstruct the complete 3D shape of the target, and the mapping between the part and the complete 3D shape is learned, then the 3D reconstruction of a single depth image is realized. (2) Learn the intermediate feature representation between the 3D real object and the reconstructed object to obtain the target potential variables in step (1). (3) The voxel floating value predicted in step (1) is transformed into binary value by using the limit learning machine to complete high-precision reconstruction.