D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 34 Citations 3,737 102 World Ranking 6448 National Ranking 591

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Algorithm

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.

His most cited work include:

  • GRASS: generative recursive autoencoders for shape structures (173 citations)
  • Fit and diverse: set evolution for inspiring 3D shape galleries (139 citations)
  • A novel quantum representation for log-polar images (118 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (67.13%)
  • Computer vision (35.66%)
  • Pattern recognition (25.17%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (67.13%)
  • Computer vision (35.66%)
  • Point cloud (16.78%)

In recent papers he was focusing on the following fields of study:

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

  • Convolution that intertwine with fields like Geometric data analysis, Octree, Algorithm and Kernel,
  • Point which is related to area like Cluster analysis, Graph and Benchmark. Kai Xu combines subjects such as 3D reconstruction, Image and Autoencoder with his study of Pattern recognition. Kai Xu interconnects Optimization problem, Deep learning and Metric in the investigation of issues within Artificial neural network.

Between 2019 and 2021, his most popular works were:

  • PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes (18 citations)
  • Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation (18 citations)
  • Learning Part Generation and Assembly for Structure-Aware Shape Synthesis. (16 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Geometry

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.

Best Publications

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)

173 Citations

GRASS: generative recursive autoencoders for shape structures

Jun Li;Kai Xu;Siddhartha Chaudhuri;Ersin Yumer.
ACM Transactions on Graphics (2017)

173 Citations

A novel quantum representation for log-polar images

Yi Zhang;Kai Lu;Yinghui Gao;Kai Xu.
Quantum Information Processing (2013)

147 Citations

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)

139 Citations

Partial intrinsic reflectional symmetry of 3D shapes

Kai Xu;Hao Zhang;Andrea Tagliasacchi;Ligang Liu.
international conference on computer graphics and interactive techniques (2009)

123 Citations

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)

122 Citations

Symmetry Hierarchy of Man‐Made Objects

Yanzhen Wang;Yanzhen Wang;Kai Xu;Kai Xu;Jun Li;Hao Zhang.
Computer Graphics Forum (2011)

116 Citations

Data-driven shape analysis and processing

Kai Xu;Vladimir G. Kim;Qixing Huang;Niloy Mitra.
international conference on computer graphics and interactive techniques (2016)

112 Citations

GRAINS: Generative Recursive Autoencoders for INdoor Scenes

Manyi Li;Akshay Gadi Patil;Kai Xu;Siddhartha Chaudhuri.
ACM Transactions on Graphics (2019)

105 Citations

An efficient and effective convolutional auto-encoder extreme learning machine network for 3d feature learning

Yueqing Wang;Zhige Xie;Kai Xu;Yong Dou.
Neurocomputing (2016)

98 Citations

Best Scientists Citing Kai Xu

Leonidas J. Guibas

Leonidas J. Guibas

Stanford University

Publications: 63

Hao Zhang

Hao Zhang

Simon Fraser University

Publications: 58

Niloy J. Mitra

Niloy J. Mitra

University College London

Publications: 49

Daniel Cohen-Or

Daniel Cohen-Or

Tel Aviv University

Publications: 48

Yu-Kun Lai

Yu-Kun Lai

Cardiff University

Publications: 28

Ligang Liu

Ligang Liu

University of Science and Technology of China

Publications: 24

Hao Su

Hao Su

University of California, San Diego

Publications: 22

Shi-Min Hu

Shi-Min Hu

Tsinghua University

Publications: 21

Thomas Funkhouser

Thomas Funkhouser

Princeton University

Publications: 20

Baoquan Chen

Baoquan Chen

Peking University

Publications: 20

Matthias Nießner

Matthias Nießner

Technical University of Munich

Publications: 19

Xin Tong

Xin Tong

Microsoft (United States)

Publications: 18

Michael Wand

Michael Wand

Johannes Gutenberg University of Mainz

Publications: 18

Peter Wonka

Peter Wonka

King Abdullah University of Science and Technology

Publications: 17

Maks Ovsjanikov

Maks Ovsjanikov

École Polytechnique

Publications: 17

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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