H
Hanspeter Pfister
Researcher at Harvard University
Publications - 499
Citations - 29142
Hanspeter Pfister is an academic researcher from Harvard University. The author has contributed to research in topics: Visualization & Rendering (computer graphics). The author has an hindex of 79, co-authored 466 publications receiving 23935 citations. Previous affiliations of Hanspeter Pfister include Mitsubishi Electric & Stony Brook University.
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Journal ArticleDOI
UpSet: Visualization of Intersecting Sets.
TL;DR: This paper introduces UpSet, a novel visualization technique for the quantitative analysis of sets, their intersections, and aggregates of intersections, focused on creating task-driven aggregates, communicating the size and properties of aggregates and intersection, and a duality between the visualization of the elements in a dataset and their set membership.
Proceedings ArticleDOI
Surfels: surface elements as rendering primitives
TL;DR: A novel method called visibility splatting determines visible surfels and holes in the z-buffer, which makes them specifically suited for low-cost, real-time graphics, such as games.
Journal ArticleDOI
Saturated Reconstruction of a Volume of Neocortex
Narayanan Kasthuri,Kenneth J. Hayworth,Daniel R. Berger,Daniel R. Berger,Richard Schalek,José Angel Conchello,Seymour Knowles-Barley,Dongil Lee,Amelio Vazquez-Reina,Verena Kaynig,Thouis R. Jones,Mike Roberts,Josh Morgan,Juan Carlos Tapia,H. Sebastian Seung,William Gray Roncal,Joshua T. Vogelstein,Randal Burns,Daniel L. Sussman,Carey E. Priebe,Hanspeter Pfister,Jeff W. Lichtman +21 more
TL;DR: In this paper, the authors describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many subcellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database.
Proceedings ArticleDOI
A data-driven reflectance model
TL;DR: This work presents a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data that lets users define perceptually meaningful parametrization directions to navigate in the reduced-dimension BRDF space.
Proceedings ArticleDOI
Blind Image Deblurring Using Dark Channel Prior
TL;DR: This work introduces a linear approximation of the min operator to compute the dark channel and achieves state-of-the-art results on deblurring natural images and compares favorably methods that are well-engineered for specific scenarios.