Permutohedral Lattice CNNs

2014/12/20 by Martin Kiefel, Kiefel, Martin, Varun Jampani +3 · 2 citations
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Infrared Target Detection Methodologies #Machine Learning (cs.LG) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Remote-Sensing Image Classification

paper · pdf · doi:10.48550/arxiv.1412.6618

openalex publication_date 2014/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 pixel position), but of more general features (such as color values). The presented algorithm makes use of the permutohedral lattice data structure. The permutohedral lattice was introduced to efficiently implement a bilateral filter, a commonly used image processing operation. Its use allows for a generalization of the convolution type found in current (spatial) convolutional network architectures.

Citations

• Gradient-based learning applied to document recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding

Cited by

• Active Convolution: Learning the Shape of Convolution for Image Classification
• Video Propagation Networks

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• Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice
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