His scientific interests lie mostly in Artificial intelligence, Segmentation, Shape analysis, Computer vision and Pattern recognition. His research in Artificial intelligence intersects with topics in Machine learning and Theoretical computer science. In his research, Discrete mathematics, Graph, Compact space and Algebra is intimately related to Homogeneous space, which falls under the overarching field of Segmentation.
His Shape analysis research includes themes of Active shape model and Cluster analysis. Kai Xu has researched Pattern recognition in several fields, including Artificial neural network, Recurrent neural network and Autoencoder. The study incorporates disciplines such as Algorithm and Interpolation in addition to Autoencoder.
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Point cloud. Kai Xu performs integrative study on Artificial intelligence and Set in his works. Many of his studies on Computer vision involve topics that are commonly interrelated, such as Computer graphics.
His Pattern recognition research is multidisciplinary, relying on both Machine learning, Cluster analysis and Autoencoder. The various areas that Kai Xu examines in his Segmentation study include Graph, Theoretical computer science, Classifier, 3d model and Shape analysis. His Point cloud research incorporates themes from Geometric data analysis, Embedding, Convolution, Algorithm and Point.
His primary areas of investigation include Artificial intelligence, Computer vision, Point cloud, Pattern recognition and Artificial neural network. His study involves Object, Feature, Segmentation, Feature extraction and Generative model, a branch of Artificial intelligence. His work on Object detection and RGB color model as part of general Computer vision research is frequently linked to Forward kinematics and Fitness function, thereby connecting diverse disciplines of science.
His study on Point cloud also encompasses disciplines like
Kai Xu spends much of his time researching Artificial intelligence, Pattern recognition, Point cloud, Computer vision and Object. In the field of Artificial intelligence, his study on Artificial neural network, Feature and Segmentation overlaps with subjects such as Rectifier. His Image segmentation study in the realm of Pattern recognition interacts with subjects such as Set.
His Point cloud research incorporates elements of Tree, Geometric data analysis, Octree and Feature learning. His work on Object detection and RGB color model as part of general Computer vision study is frequently connected to Code and Point, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Object research focuses on Feature extraction and how it connects with Representation, Embedding, Generative model and Pose.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
GRASS: generative recursive autoencoders for shape structures
Jun Li;Kai Xu;Siddhartha Chaudhuri;Ersin Yumer.
ACM Transactions on Graphics (2017)
Fit and diverse: set evolution for inspiring 3D shape galleries
Kai Xu;Hao Zhang;Daniel Cohen-Or;Baoquan Chen.
international conference on computer graphics and interactive techniques (2012)
A novel quantum representation for log-polar images
Yi Zhang;Kai Lu;Yinghui Gao;Kai Xu.
Quantum Information Processing (2013)
Style-content separation by anisotropic part scales
Kai Xu;Honghua Li;Hao Zhang;Daniel Cohen-Or.
international conference on computer graphics and interactive techniques (2010)
Photo-inspired model-driven 3D object modeling
Kai Xu;Hanlin Zheng;Hao Zhang;Daniel Cohen-Or.
international conference on computer graphics and interactive techniques (2011)
Partial intrinsic reflectional symmetry of 3D shapes
Kai Xu;Hao Zhang;Andrea Tagliasacchi;Ligang Liu.
international conference on computer graphics and interactive techniques (2009)
Symmetry Hierarchy of Man‐Made Objects
Yanzhen Wang;Yanzhen Wang;Kai Xu;Kai Xu;Jun Li;Hao Zhang.
Computer Graphics Forum (2011)
Data-driven shape analysis and processing
Kai Xu;Vladimir G. Kim;Qixing Huang;Niloy Mitra.
international conference on computer graphics and interactive techniques (2016)
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
Manyi Li;Akshay Gadi Patil;Kai Xu;Siddhartha Chaudhuri.
ACM Transactions on Graphics (2019)
An efficient and effective convolutional auto-encoder extreme learning machine network for 3d feature learning
Yueqing Wang;Zhige Xie;Kai Xu;Yong Dou.
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