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Hendrik P. A. Lensch

Hendrik P. A. Lensch

D-Index & Metrics

Computer Science

D-Index
47
Citations
7871
World Ranking
6560
National Ranking
309

Overview

Hendrik P. A. Lensch is affiliated with the University of Tübingen in Germany and specializes in computer science with a significant focus on computer vision, artificial intelligence, and computer graphics. Their research spans several subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Experimental and Cognitive Psychology, and Computational Mechanics.

The scientist's recent publications reveal a strong engagement with advanced topics in vision and imaging as well as computational techniques for 3D modeling. Notable recent papers include:

  • "GGNN: Graph-Based GPU Nearest Neighbor Search" (2022) published in IEEE Transactions on Big Data
  • "SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections" (2022) published on arXiv (Cornell University)
  • "Robust fitting of parallax-aware mixtures for path guiding" (2020) published in ACM Transactions on Graphics
  • "NeRD: Neural Reflectance Decomposition from Image Collections" (2021) presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Tetrahedra of varying density and their applications" (2021) published in The Visual Computer

Their scholarly output demonstrates attention to computational methods in computer graphics and advanced imaging processes, with a particular interest in multimodal machine learning applications and 3D shape modeling and analysis.

Frequently collaborating with other researchers, Hendrik P. A. Lensch works regularly with several co-authors, including:

  • Hassan Shahmohammadi
  • R. Harald Baayen
  • Simon Doll
  • Mark Boss
  • Leonard Salewski

Publication venues frequently chosen reflect their areas of focus, including:

  • arXiv (Cornell University)
  • The Visual Computer
  • IEEE Robotics and Automation Letters
  • Lecture Notes in Computer Science
  • IEEE Transactions on Big Data

The scientist's primary topics of research cover:

  • Multimodal Machine Learning Applications
  • Advanced Vision and Imaging
  • Computer Graphics and Visualization Techniques
  • Language, Metaphor, and Cognition
  • Natural Language Processing Techniques
  • 3D Shape Modeling and Analysis
  • Topic Modeling

Hendrik P. A. Lensch's work contributes to a broad range of computational and applied research areas within computer science, consistently exploring intersections between machine learning, vision, and graphics techniques.

Best Publications

  • Image-based reconstruction of spatial appearance and geometric detail

    Hendrik P. A. Lensch;Jan Kautz;Michael Goesele;Wolfgang Heidrich

  • Dual photography

    Pradeep Sen;Billy Chen;Gaurav Garg;Stephen R. Marschner

  • NeRD: Neural Reflectance Decomposition From Image Collections

    Mark Boss;Raphael Braun;Varun Jampani;Jonathan T. Barron

  • Polarization and Phase-Shifting for 3D Scanning of Translucent Objects

    Tongbo Chen;H.P.A. Lensch;C. Fuchs;H.-P. Seidel

  • Optimal HDR reconstruction with linear digital cameras

    Miguel Granados;Boris Ajdin;Michael Wand;Christian Theobalt

  • Principles of Appearance Acquisition and Representation

    Tim Weyrich;Jason Lawrence;Hendrik Lensch;Szymon Rusinkiewicz

  • Transparent and Specular Object Reconstruction

    Ivo Ihrke;Kiriakos N. Kutulakos;Hendrik P. A. Lensch;Marcus A. Magnor

  • Image-based reconstruction of spatially varying materials

    Hendrik P. A. Lensch;Jan Kautz;Michael Goesele;Wolfgang Heidrich

  • The Frankencamera: an experimental platform for computational photography

    Andrew Adams;Eino-Ville Talvala;Sung Hee Park;David E. Jacobs

  • DISCO: acquisition of translucent objects

    Michael Goesele;Hendrik P. A. Lensch;Jochen Lang;Christian Fuchs

  • A Silhouette-Based Algorithm for Texture Registration and Stitching

    Hendrik P.A. Lensch;Wolfgang Heidrich;Hans-Peter Seidel

  • Automated texture registration and stitching for real world models

    H.P.A. Lensch;W. Heidrich;H.-P. Seidel

  • Learning Blind Motion Deblurring

    Patrick Wieschollek;Michael Hirsch;Bernhard Scholkopf;Hendrik P.A. Lensch

  • Infrared Colorization Using Deep Convolutional Neural Networks

    Matthias Limmer;Hendrik P. A. Lensch

  • Tempest in a Teapot: Compromising Reflections Revisited

    Michael Backes;Tongbo Chen;Markus Duermuth;Hendrik P.A. Lensch

  • 3D Acquisition of mirroring objects using striped patterns

    Marco Tarini;Hendrik P. A. Lensch;Michael Goesele;Hans-Peter Seidel

  • Acquisition and analysis of bispectral bidirectional reflectance distribution functions

    Matthias B. Hullin;Boris Ajdin;Johannes Hanika;Hans-Peter Seidel

  • Edge-avoiding À-Trous wavelet transform for fast global illumination filtering

    Holger Dammertz;Daniel Sewtz;Johannes Hanika;Hendrik P. A. Lensch

  • Interactive rendering of translucent objects

    H.P.A. Lensch;M. Goesele;P. Bekaert;J. Kautz

  • Interactive Rendering of Translucent Objects

    Hendrik P.A. Lensch;Michael Goesele;Philippe Bekaert;Jan Kautz

  • Modulated phase-shifting for 3D scanning

    Tongbo Chen;H.-P. Seidel;H. Lensch

  • Acquisition and analysis of bispectral bidirectional reflectance and reradiation distribution functions

    Matthias B. Hullin;Johannes Hanika;Boris Ajdin;Hans-Peter Seidel

Frequent Co-Authors

Hans-Peter Seidel
Hans-Peter Seidel Max Planck Institute for Informatics
Michael Goesele
Michael Goesele Technical University of Darmstadt
Jan Kautz
Jan Kautz Nvidia (United States)
Wolfgang Heidrich
Wolfgang Heidrich King Abdullah University of Science and Technology
Marcus Magnor
Marcus Magnor Technische Universität Braunschweig
Szymon Rusinkiewicz
Szymon Rusinkiewicz Princeton University
Martin V. Butz
Martin V. Butz University of Tübingen
Marc Levoy
Marc Levoy Google (United States)
Christian Theobalt
Christian Theobalt Max Planck Institute for Informatics

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