Search Results for author: Renato Pajarola

Found 6 papers, 5 papers with code

PPSURF: Combining Patches and Point Convolutions for Detailed Surface Reconstruction

1 code implementation16 Jan 2024 Philipp Erler, Lizeth Fuentes, Pedro Hermosilla, Paul Guerrero, Renato Pajarola, Michael Wimmer

3D surface reconstruction from point clouds is a key step in areas such as content creation, archaeology, digital cultural heritage, and engineering.

Surface Reconstruction

Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories

no code implementations27 Feb 2021 Claudio Mura, Renato Pajarola, Konrad Schindler, Niloy Mitra

Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces.

Image-to-Image Translation Management

Visualization of High-dimensional Scalar Functions Using Principal Parameterizations

1 code implementation11 Sep 2018 Rafael Ballester-Ripoll, Renato Pajarola

Insightful visualization of multidimensional scalar fields, in particular parameter spaces, is key to many fields in computational science and engineering.

Dimensionality Reduction Tensor Decomposition +1

TTHRESH: Tensor Compression for Multidimensional Visual Data

2 code implementations15 Jun 2018 Rafael Ballester-Ripoll, Peter Lindstrom, Renato Pajarola

Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies.

Graphics

Tensor Approximation of Advanced Metrics for Sensitivity Analysis

1 code implementation5 Dec 2017 Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola

Following up on the success of the analysis of variance (ANOVA) decomposition and the Sobol indices (SI) for global sensitivity analysis, various related quantities of interest have been defined in the literature including the effective and mean dimensions, the dimension distribution, and the Shapley values.

Numerical Analysis 65C20, 15A69, 49Q12

Sobol Tensor Trains for Global Sensitivity Analysis

1 code implementation1 Dec 2017 Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola

We propose the tensor train decomposition (TT) as a unified framework for surrogate modeling and global sensitivity analysis via Sobol indices.

Numerical Analysis 65C20, 15A69, 49Q12

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