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Thomas Funkhouser

Researcher at Google

Publications -  219
Citations -  38445

Thomas Funkhouser is an academic researcher from Google. The author has contributed to research in topics: Rendering (computer graphics) & Computer science. The author has an hindex of 79, co-authored 209 publications receiving 30242 citations. Previous affiliations of Thomas Funkhouser include Agere Systems & Bell Labs.

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ShapeNet: An Information-Rich 3D Model Repository

TL;DR: ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy, a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations.
Proceedings ArticleDOI

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

TL;DR: This work introduces ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations, and shows that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks.
Journal ArticleDOI

Shape distributions

TL;DR: The dissimilarities between sampled distributions of simple shape functions provide a robust method for discriminating between classes of objects in a moderately sized database, despite the presence of arbitrary translations, rotations, scales, mirrors, tessellations, simplifications, and model degeneracies.
Proceedings ArticleDOI

The Princeton Shape Benchmark

TL;DR: It is concluded that no single descriptor is best for all classifications, and thus the main contribution of this paper is to provide a framework to determine the conditions under which each descriptor performs best.
Proceedings ArticleDOI

Rotation invariant spherical harmonic representation of 3D shape descriptors

TL;DR: The limitations of canonical alignment are described and an alternate method, based on spherical harmonics, for obtaining rotation invariant representations is discussed, which reduces the dimensionality of the descriptor, providing a more compact representation, which in turn makes comparing two models more efficient.