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 38 Citations 8,555 92 World Ranking 4978 National Ranking 2457

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Algorithm

Qixing Huang focuses on Artificial intelligence, Computer vision, Machine learning, Shape analysis and Algorithm. His Artificial intelligence study frequently intersects with other fields, such as Pattern recognition. His Machine learning research incorporates elements of Annotation, Data mining, Data science and Conditional random field.

His Algorithm study combines topics in areas such as Iterated function, Segmentation and Mathematical optimization. His Segmentation research includes elements of Matching, Surface, Pairwise comparison and Feature. Qixing Huang studied Surface reconstruction and Computer graphics that intersect with Benchmark.

His most cited work include:

  • ShapeNet: An Information-Rich 3D Model Repository (1726 citations)
  • A scalable active framework for region annotation in 3D shape collections (428 citations)
  • Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach (302 citations)

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

Qixing Huang mostly deals with Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Object. His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Shape analysis and Data mining. His biological study spans a wide range of topics, including Computer graphics and Metric.

The Algorithm study combines topics in areas such as Object matching, Mathematical optimization, Graph and Regular polygon. His work carried out in the field of Pattern recognition brings together such families of science as Ground truth and Regularization. In his study, Computer engineering is strongly linked to Image, which falls under the umbrella field of Object.

He most often published in these fields:

  • Artificial intelligence (67.69%)
  • Computer vision (33.85%)
  • Algorithm (23.08%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (67.69%)
  • Artificial neural network (13.08%)
  • Pattern recognition (23.08%)

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

Qixing Huang mainly investigates Artificial intelligence, Artificial neural network, Pattern recognition, Object and Pose. His Artificial intelligence research integrates issues from Algorithm and Computer vision. His Artificial neural network research is multidisciplinary, incorporating perspectives in Contextual image classification, Optimization problem, Generative grammar and Graph.

His studies in Pattern recognition integrate themes in fields like Regularization and Image. As a part of the same scientific family, he mostly works in the field of Object, focusing on Matching and, on occasion, Iterative method. His Pose research also works with subjects such as

  • Robustness together with Pixel,
  • RGB color model that connect with fields like Robotics and Computer graphics.

Between 2017 and 2021, his most popular works were:

  • PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation (194 citations)
  • Domain Transfer Through Deep Activation Matching (70 citations)
  • StarMap for Category-Agnostic Keypoint and Viewpoint Estimation (39 citations)

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

  • Artificial intelligence
  • Algorithm
  • Computer vision

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Pose, Key and Artificial neural network. His work on Object, Benchmark and Task is typically connected to Fusion as part of general Artificial intelligence study, connecting several disciplines of science. His Benchmark research integrates issues from Robotics, Computer graphics, Deep learning, RGB color model and Generative model.

His study in the fields of Segmentation and Unsupervised learning under the domain of Pattern recognition overlaps with other disciplines such as Domain adaptation. Qixing Huang focuses mostly in the field of Pose, narrowing it down to matters related to Robustness and, in some cases, Pixel and Outlier. His studies deal with areas such as Range, Transformation, Discriminative model and Coordinate system as well as Artificial neural network.

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

ShapeNet: An Information-Rich 3D Model Repository

Angel X. Chang;Thomas A. Funkhouser;Leonidas J. Guibas;Pat Hanrahan.
arXiv: Graphics (2015)

1236 Citations

A scalable active framework for region annotation in 3D shape collections

Li Yi;Vladimir G. Kim;Duygu Ceylan;I-Chao Shen.
international conference on computer graphics and interactive techniques (2016)

428 Citations

Reassembling fractured objects by geometric matching

Qi-Xing Huang;Simon Flöry;Natasha Gelfand;Michael Hofer.
international conference on computer graphics and interactive techniques (2006)

426 Citations

Non-rigid registration under isometric deformations

Qi-Xing Huang;Bart Adams;Martin Wicke;Leonidas J. Guibas.
symposium on geometry processing (2008)

318 Citations

Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach

Xingyi Zhou;Qixing Huang;Xiao Sun;Xiangyang Xue.
international conference on computer vision (2017)

308 Citations

Integral invariants for robust geometry processing

Helmut Pottmann;Johannes Wallner;Qi-Xing Huang;Yong-Liang Yang.
Computer Aided Geometric Design (2009)

267 Citations

Geometry and Convergence Analysis of Algorithms for Registration of 3D Shapes

Helmut Pottmann;Qi-Xing Huang;Yong-Liang Yang;Shi-Min Hu.
International Journal of Computer Vision (2006)

226 Citations

Structure-aware shape processing

Niloy J. Mitra;Michael Wand;Hao Zhang;Daniel Cohen-Or.
international conference on computer graphics and interactive techniques (2013)

223 Citations

Learning Dense Correspondence via 3D-Guided Cycle Consistency

Tinghui Zhou;Philipp Krahenbuhl;Mathieu Aubry;Qixing Huang.
computer vision and pattern recognition (2016)

216 Citations

PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation

Sida Peng;Yuan Liu;Qixing Huang;Xiaowei Zhou.
computer vision and pattern recognition (2019)

212 Citations

Best Scientists Citing Qixing Huang

Leonidas J. Guibas

Leonidas J. Guibas

Stanford University

Publications: 137

Hao Zhang

Hao Zhang

Simon Fraser University

Publications: 67

Niloy J. Mitra

Niloy J. Mitra

University College London

Publications: 65

Maks Ovsjanikov

Maks Ovsjanikov

École Polytechnique

Publications: 55

Daniel Cohen-Or

Daniel Cohen-Or

Tel Aviv University

Publications: 53

Hao Su

Hao Su

University of California, San Diego

Publications: 53

Michael M. Bronstein

Michael M. Bronstein

Imperial College London

Publications: 52

Christian Theobalt

Christian Theobalt

Max Planck Institute for Informatics

Publications: 49

Kai Xu

Kai Xu

National University of Defense Technology

Publications: 42

Thomas Funkhouser

Thomas Funkhouser

Princeton University

Publications: 42

Emanuele Rodolà

Emanuele Rodolà

Sapienza University of Rome

Publications: 39

Andreas Geiger

Andreas Geiger

University of Tübingen

Publications: 38

Matthias Nießner

Matthias Nießner

Technical University of Munich

Publications: 37

Baoquan Chen

Baoquan Chen

Peking University

Publications: 36

Yu-Kun Lai

Yu-Kun Lai

Cardiff University

Publications: 35

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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