World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
99
Citations
48435
World Ranking
386
National Ranking
213

Research.com Recognitions

  • 2018 - ACM Fellow For research contributions in computer graphics
  • 1999 - Fellow of Alfred P. Sloan Foundation

Overview

Thomas Funkhouser is affiliated with Google in the United States and has a substantial body of research primarily in computer science and engineering. Their work spans multiple subfields, including computer vision and pattern recognition, computer graphics and computer-aided design, computational mechanics, control and systems engineering, and artificial intelligence.

Funkhouser's research topics cover a range of advanced technical areas. These include:

  • Computer Graphics and Visualization Techniques
  • 3D Shape Modeling and Analysis
  • Advanced Vision and Imaging
  • Advanced Neural Network Applications
  • Human Pose and Action Recognition
  • Robot Manipulation and Learning
  • Robotics and Sensor-Based Localization

The scientist has contributed numerous papers to highly regarded publication venues. Frequent venues where they have published include:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Robotics and Automation Letters
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Zenodo (CERN European Organization for Nuclear Research)

Some of their recent papers are:

  • "ShapeNet: An Information-Rich 3D Model Repository," 2023, Zenodo (CERN European Organization for Nuclear Research)
  • "TossingBot: Learning to Throw Arbitrary Objects With Residual Physics," 2020, IEEE Transactions on Robotics
  • "Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost Demonstrations," 2020, IEEE Robotics and Automation Letters
  • "TidyBot: personalized robot assistance with large language models," 2023, Autonomous Robots

Collaboration is a notable aspect of Funkhouser's career. Frequent co-authors include:

  • Kyle Genova
  • Abhijit Kundu
  • Alireza Fathi
  • Shuran Song
  • Caroline Pantofaru

Funkhouser has received recognition in their field, having been named an ACM Fellow in 2018 for research contributions in computer graphics. Earlier in their career, they were honored as a Fellow of the Alfred P. Sloan Foundation in 1999.

Best Publications

  • ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

    Angela Dai;Angel X. Chang;Manolis Savva;Maciej Halber

  • Shape distributions

    Robert Osada;Thomas Funkhouser;Bernard Chazelle;David Dobkin

  • ShapeNet: An Information-Rich 3D Model Repository

    Angel X. Chang;Thomas A. Funkhouser;Leonidas J. Guibas;Pat Hanrahan

  • The Princeton Shape Benchmark

    P. Shilane;P. Min;M. Kazhdan;T. Funkhouser

  • Rotation invariant spherical harmonic representation of 3D shape descriptors

    Michael Kazhdan;Thomas Funkhouser;Szymon Rusinkiewicz

  • Dilated Residual Networks

    Fisher Yu;Vladlen Koltun;Thomas Funkhouser

  • Matterport3D: Learning from RGB-D Data in Indoor Environments

    Angel Chang;Angela Dai;Thomas Funkhouser;Maciej Halber

  • A search engine for 3D models

    Thomas Funkhouser;Patrick Min;Michael Kazhdan;Joyce Chen

  • Semantic Scene Completion from a Single Depth Image

    Shuran Song;Fisher Yu;Andy Zeng;Angel X. Chang

  • 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

    Andy Zeng;Shuran Song;Matthias NieBner;Matthew Fisher

  • Adaptive display algorithm for interactive frame rates during visualization of complex virtual environments

    Thomas A. Funkhouser;Carlo H. Séquin

  • Matching 3D models with shape distributions

    R. Osada;T. Funkhouser;B. Chazelle;D. Dobkin;D. Dobkin

  • ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

    Angela Dai;Angel X. Chang;Manolis Savva;Maciej Halber

  • A benchmark for 3D mesh segmentation

    Xiaobai Chen;Aleksey Golovinskiy;Thomas Funkhouser

  • Modeling by example

    Thomas Funkhouser;Michael Kazhdan;Philip Shilane;Patrick Min

  • IBRNet: Learning Multi-View Image-Based Rendering

    Qianqian Wang;Zhicheng Wang;Kyle Genova;Pratul Srinivasan

  • Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning

    Andy Zeng;Shuran Song;Stefan Welker;Johnny Lee

  • Shape-based recognition of 3D point clouds in urban environments

    Aleksey Golovinskiy;Vladimir G. Kim;Thomas Funkhouser

  • Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

    Andy Zeng;Shuran Song;Kuan-Ting Yu;Elliott Donlon

  • Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

    John A. Capra;Roman A. Laskowski;Janet M. Thornton;Mona Singh

  • 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

    Andy Zeng;Shuran Song;Matthias Nießner;Matthew Fisher

  • Local Implicit Grid Representations for 3D Scenes

    Chiyu Max Jiang;Avneesh Sud;Ameesh Makadia;Jingwei Huang

Frequent Co-Authors

Shuran Song
Shuran Song Stanford University
Szymon Rusinkiewicz
Szymon Rusinkiewicz Princeton University
Michael Kazhdan
Michael Kazhdan Johns Hopkins University
Manolis Savva
Manolis Savva Simon Fraser University
Yinda Zhang
Yinda Zhang Google (United States)
Angel X. Chang
Angel X. Chang Simon Fraser University
Vladimir G. Kim
Vladimir G. Kim Adobe Systems (United States)
Yaron Lipman
Yaron Lipman Facebook (United States)
Leonidas J. Guibas
Leonidas J. Guibas Stanford University
Adam Finkelstein
Adam Finkelstein Princeton University

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