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 30 Citations 10,817 60 World Ranking 8692 National Ranking 365

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary areas of investigation include Artificial intelligence, Computer vision, Natural language processing, Information retrieval and Object. His studies in RGB color model and Voxel are all subfields of Artificial intelligence research. His study ties his expertise on Semantics together with the subject of Computer vision.

The study incorporates disciplines such as Resolution and Coreference in addition to Natural language processing. In the field of Information retrieval, his study on WordNet overlaps with subjects such as Technical report. His studies in Object integrate themes in fields like Taxonomy and Benchmark.

His most cited work include:

  • ShapeNet: An Information-Rich 3D Model Repository (1726 citations)
  • ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (756 citations)
  • Semantic Scene Completion from a Single Depth Image (669 citations)

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

His primary scientific interests are in Artificial intelligence, Natural language processing, Object, Computer vision and Natural language. His work on RGB color model, Semantics and Segmentation as part of general Artificial intelligence study is frequently linked to Set, therefore connecting diverse disciplines of science. In RGB color model, he works on issues like Computer graphics, which are connected to Taxonomy and Data visualization.

His studies examine the connections between Natural language processing and genetics, as well as such issues in Inference, with regards to Consistency. His Object research is multidisciplinary, relying on both Point cloud, Representation, Human–computer interaction, Deep learning and Information retrieval. His work in the fields of Voxel overlaps with other areas such as Scene statistics, Viewing frustum and Depth map.

He most often published in these fields:

  • Artificial intelligence (84.13%)
  • Natural language processing (38.10%)
  • Object (23.81%)

What were the highlights of his more recent work (between 2018-2020)?

  • Artificial intelligence (84.13%)
  • Object (23.81%)
  • Human–computer interaction (12.70%)

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

The scientist’s investigation covers issues in Artificial intelligence, Object, Human–computer interaction, Point cloud and Computer vision. In his works, Angel X. Chang undertakes multidisciplinary study on Artificial intelligence and Robot learning. He performs integrative Object and Set research in his work.

His study looks at the relationship between Point cloud and topics such as Object detection, which overlap with Pattern recognition, Representation and Autoencoder. The concepts of his Computer vision study are interwoven with issues in Sentence, Learning object and Natural language. His study in RGB color model is interdisciplinary in nature, drawing from both Margin and Spatial relation.

Between 2018 and 2020, his most popular works were:

  • PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding (180 citations)
  • Scan2CAD: Learning CAD Model Alignment in RGB-D Scans (77 citations)
  • SAPIEN: A SimulAted Part-Based Interactive ENvironment (38 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary areas of study are Artificial intelligence, Segmentation, Categorization, Deep learning and Machine learning. Artificial intelligence and Computer vision are commonly linked in his work. He combines subjects such as Computer graphics and Benchmark with his study of Computer vision.

Angel X. Chang has included themes like Shape analysis and Big data in his Segmentation study. His Robot learning research covers fields of interest such as Robotics, Open research, Heuristic, Task analysis and Task. His study connects 3D reconstruction and RGB color model.

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

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

Angela Dai;Angel X. Chang;Manolis Savva;Maciej Halber.
computer vision and pattern recognition (2017)

634 Citations

Semantic Scene Completion from a Single Depth Image

Shuran Song;Fisher Yu;Andy Zeng;Angel X. Chang.
computer vision and pattern recognition (2017)

511 Citations

Deterministic coreference resolution based on entity-centric, precision-ranked rules

Heeyoung Lee;Angel Chang;Yves Peirsman;Nathanael Chambers.
Computational Linguistics (2013)

485 Citations

ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

Angela Dai;Angel X. Chang;Manolis Savva;Maciej Halber.
arXiv: Computer Vision and Pattern Recognition (2017)

470 Citations

Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task

Heeyoung Lee;Yves Peirsman;Angel Chang;Nathanael Chambers.
conference on computational natural language learning (2011)

459 Citations

SUTime: A library for recognizing and normalizing time expressions

Angel X. Chang;Christopher Manning.
language resources and evaluation (2012)

450 Citations

Matterport3D: Learning from RGB-D Data in Indoor Environments

Angel Chang;Angela Dai;Thomas Funkhouser;Maciej Halber.
international conference on 3d vision (2017)

327 Citations

On Evaluation of Embodied Navigation Agents

Peter Anderson;Angel X. Chang;Devendra Singh Chaplot;Alexey Dosovitskiy.
arXiv: Artificial Intelligence (2018)

304 Citations

Matterport3D: Learning from RGB-D Data in Indoor Environments

Angel Chang;Angela Dai;Thomas Funkhouser;Maciej Halber.
arXiv: Computer Vision and Pattern Recognition (2017)

281 Citations

Best Scientists Citing Angel X. Chang

Leonidas J. Guibas

Leonidas J. Guibas

Stanford University

Publications: 121

Hao Zhang

Hao Zhang

Simon Fraser University

Publications: 52

Hao Su

Hao Su

University of California, San Diego

Publications: 51

Kai Xu

Kai Xu

National University of Defense Technology

Publications: 49

Niloy J. Mitra

Niloy J. Mitra

University College London

Publications: 48

Silvio Savarese

Silvio Savarese

Stanford University

Publications: 47

Matthias Nießner

Matthias Nießner

Technical University of Munich

Publications: 47

Dhruv Batra

Dhruv Batra

Georgia Institute of Technology

Publications: 45

Jitendra Malik

Jitendra Malik

University of California, Berkeley

Publications: 42

Andreas Geiger

Andreas Geiger

University of Tübingen

Publications: 41

Thomas Funkhouser

Thomas Funkhouser

Princeton University

Publications: 40

Federico Tombari

Federico Tombari

Technical University of Munich

Publications: 40

Kristen Grauman

Kristen Grauman

Facebook (United States)

Publications: 40

Joshua B. Tenenbaum

Joshua B. Tenenbaum

MIT

Publications: 38

Jiajun Wu

Jiajun Wu

Stanford University

Publications: 38

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.

If you think any of the details on this page are incorrect, let us know.

Contact us
Something went wrong. Please try again later.