H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 33 Citations 10,217 57 World Ranking 6663 National Ranking 292

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, Object, Information retrieval and RGB color model. As part of one scientific family, Manolis Savva deals mainly with the area of Artificial intelligence, narrowing it down to issues related to the Machine learning, and often Procedural modeling. Computer vision is closely attributed to Artificial neural network in his study.

The various areas that Manolis Savva examines in his Object study include Computer graphics, Data visualization and Benchmark. His Information retrieval study integrates concerns from other disciplines, such as Deep learning and Taxonomy. His Voxel research incorporates elements of Depth map, Leverage and Viewing frustum.

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?

Manolis Savva mainly investigates Artificial intelligence, Object, Computer vision, RGB color model and Human–computer interaction. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition. His study in Object is interdisciplinary in nature, drawing from both Representation and Deep learning.

His Deep learning research includes themes of Artificial neural network and Information retrieval. His research investigates the connection between Computer vision and topics such as Computer graphics that intersect with issues in Taxonomy, Data visualization and WordNet. Manolis Savva works mostly in the field of RGB color model, limiting it down to topics relating to Leverage and, in certain cases, Depth map, Viewing frustum and Voxel, as a part of the same area of interest.

He most often published in these fields:

  • Artificial intelligence (65.71%)
  • Object (24.29%)
  • Computer vision (22.86%)

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

  • Human–computer interaction (15.71%)
  • Artificial intelligence (65.71%)
  • Reinforcement learning (12.86%)

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

Manolis Savva focuses on Human–computer interaction, Artificial intelligence, Reinforcement learning, Task and Object. Manolis Savva interconnects Annotation, Motion and Solid modeling in the investigation of issues within Human–computer interaction. Manolis Savva studies Artificial intelligence, focusing on Relational graph in particular.

His Reinforcement learning research is multidisciplinary, incorporating elements of RGB color model and Computer engineering. His RGB color model research focuses on subjects like Distributed computing, which are linked to Leverage. His studies in Computer engineering integrate themes in fields like Frame rate and Task.

Between 2019 and 2021, his most popular works were:

  • DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames (57 citations)
  • ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects. (17 citations)
  • Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? (12 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

His main research concerns Embodied cognition, Task, Human–computer interaction, Object and Software deployment. Embodied cognition is connected with Group, State and Focus in his study. His Software deployment study spans across into subjects like Machine learning, Artificial intelligence, Bridge, Code and Space.

His research on Machine learning often connects related topics like Robot. Manolis Savva has included themes like RGB color model and Reinforcement learning in his Robot study.

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.

Top 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

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

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

Example-based synthesis of 3D object arrangements

Matthew Fisher;Daniel Ritchie;Manolis Savva;Thomas Funkhouser.
international conference on computer graphics and interactive techniques (2012)

308 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

Back-action-evading measurements of nanomechanical motion

J. B. Hertzberg;J. B. Hertzberg;T. Rocheleau;T. Ndukum;M. Savva.
Nature Physics (2010)

263 Citations

MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments

Manolis Savva;Angel X. Chang;Alexey Dosovitskiy;Thomas A. Funkhouser.
arXiv: Learning (2017)

232 Citations

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

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