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Image-based 3D reconstruction is widely used in practice due to less hardware constraints,lower cost and higher flexibility.Especially for the problems of sparseness and uneven density of the three-dimensional point cloud data generated by the image due to the occlusion between various parts of the object,it has always been a difficulty and hot issue to deal with.In this paper,a mask-based sparse point cloud filtering algorithm is proposed.Firstly,the bounding box of the point cloud is calculated and the grid is adaptively divided according to the sparseness of the point cloud.Secondly,Depth-first search is used to recursively find all customized connected domains composed of grids generated at the first step.Then adaptively calculating the threshold based on the quantized importance index,selecting the connected domains that should be retained based on the adaptive threshold,and defining the set of all retained connected domains as a mask,which is used to describe the global spatial topology information of the sparse point cloud.Finally,points covered by the mask are retained while points of the uncovered area are removed,so as to filter the outliers.This method can handle the point cloud data generated by occlusion and with great differences in spatial density.It can effectively remove outliers in the original three-dimensional point cloud data,while maintaining the detailed information of the point cloud.

Seismic data plays a vital role in oil and gas exploration and geological exploration.Accurate and detailed seismic data can help to provide accurate guidance for oil and gas exploration,reduce the risk of exploration,and generate huge social and economic benefits.In terms of improving the resolution of seismic data,the existing methods are difficult to recover detailed geolo-gical information when facing large amounts of data,and have poor results in high-resolution recovery,denoising performance and efficiency,and it is difficult to meet the actual needs.Seismic data can reflect the composition of geological structures and strata,and have the characteristics of high local correlation and low global correlation.At the same time,the high frequency part of seismic data usually contain important geological exploration information such as layering,chasm,etc.In view of the characteristics of seismic data,this paper innovatively transforms the problem of seismic data reconstruction into the problem of image super-resolution,and proposes a method for super-resolution of seismic data based on the generative adversarial networks.In view of the characteristics of high local correlation and low global correlation of seismic data distribution,a residual attention module is designed to mine the inherent correlation of seismic data,so as to recover more refined seismic data.By training a generative adversarial network model with a relative generative adversarial loss function,the generative network is used to perform super-resolution recovery of the seismic data to recover more refined seismic data.In this paper,experimental verification is carried out on real seismic data,and the experimental results show that the proposed method has a good effect on super-resolution of seismic data and has strong practicability.

We present an inverse kinematics solver based on Gaussian process latent variable models (GP-LVM). Because of the high-dimension of motion capture data, Analyzing them directly is a very hard work. We map the motion capture data from higher-dimensional observation space to two-dimensional latent space based on GP-LVM, then, find out the representative poses of virtual character by clustering the motion capture data in latent space. Finally, weight the representative poses and optimize the weights, combined with constraints on the end effectors, in order to synthesize the optimized pose. The experiments show that our method obtains satisfying effects. [ABSTRACT FROM AUTHOR]