Fast High-Dimensional Filtering Using the Permutohedral Lattice

2010-05-03 · Eurographics 2010 5 · Andrew Adams, Jongmin Baek, Myers Abraham Davis

Many useful algorithms for processing images and geometry fall under the general framework of high-dimensional Gaussian filtering. This family of algorithms includes bilateral filtering and non-local means. We propose a new way to perform such filters using the permutohedral lattice, which tessellates high-dimensional space with uniform simplices. Our algorithm is the first implementation of a high-dimensional Gaussian filter that is both linear in input size and polynomial in dimensionality. Furthermore it is parameter-free, apart from the filter size, and achieves a consistently high accuracy relative to ground truth (> 45 dB). We use this to demonstrate a number of interactive-rate applications of filters in as high as eight dimensions.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Vocal Bursts Intensity Prediction

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes

2021-06-12 · Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson

State-of-the-art methods for scalable Gaussian processes use iterative algorithms, requiring fast matrix vector multiplies (MVMs) with the covariance kernel. The Structured Kernel Interpolation (SKI) framework accelerate…

Gaussian Processes GPU

Permutohedral Lattice CNNs

2014-12-20 · Martin Kiefel, Varun Jampani, Peter V. Gehler

This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like p…

Position

LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

2019-12-12 · Radu Alexandru Rosu, Peer Schütt, Jan Quenzel, Sven Behnke

Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images. However, applying the same methods on 3D data still poses challenges due to the heavy memory req…

3D Semantic Segmentation Point Cloud Segmentation Segmentation Semantic Segmentation

Permutohedral-GCN: Graph Convolutional Networks with Global Attention

2020-03-02 · Hesham Mostafa, Marcel Nassar

Graph convolutional networks (GCNs) update a node's feature vector by aggregating features from its neighbors in the graph. This ignores potentially useful contributions from distant nodes. Identifying such useful distan…

Node Classification

LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices

2021-08-09 · Radu Alexandru Rosu, Peer Schütt, Jan Quenzel, Sven Behnke

Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images. Applying the same methods on 3D data still poses challenges due to the heavy memory requirements…