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Permutohedral Lattice CNNs [article]

Martin Kiefel, Varun Jampani, Peter V. Gehler
2015 arXiv   pre-print
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.  ...  SPARSE CNNS AND ENCODING INVARIANTS The permutohedral convolution can be used as a new building block in a CNN architecture.  ... 
arXiv:1412.6618v3 fatcat:iuh5k2wh5vaqdmsamyoqxm5jj4

Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks

Varun Jampani, Martin Kiefel, Peter V. Gehler
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We build on the permutohedral lattice construction for efficient filtering. The ability to learn more general forms of high-dimensional filters can be used in several diverse applications.  ...  Finally, we introduce layers of bilateral filters in CNNs and propose bilateral neural networks for the use of highdimensional sparse data.  ...  Figure 2 . 2 Visualization of the Permutohedral Lattice.  ... 
doi:10.1109/cvpr.2016.482 dblp:conf/cvpr/JampaniKG16 fatcat:gfjx6y54yzen7nunorw4b42rde

Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks [article]

Varun Jampani and Martin Kiefel and Peter V. Gehler
2015 arXiv   pre-print
We build on the permutohedral lattice construction for efficient filtering. The ability to learn more general forms of high-dimensional filters can be used in several diverse applications.  ...  Finally, we introduce layers of bilateral filters in CNNs and propose bilateral neural networks for the use of high-dimensional sparse data.  ...  A permutohedral lattice is the tessellation of space into permutohedral simplices. We refer to [2] for details of the lattice construction and its properties.  ... 
arXiv:1503.04949v3 fatcat:wcfekolyyvcmtar6jorisvahli

Permutohedral Attention Module for Efficient Non-local Neural Networks [chapter]

Samuel Joutard, Reuben Dorent, Amanda Isaac, Sebastien Ourselin, Tom Vercauteren, Marc Modat
2019 Lecture Notes in Computer Science  
Segmentation and labeling is now typically done with convolutional neural networks (CNNs) but the context of the CNN is limited by the receptive field which itself is limited by memory requirements and  ...  In this paper, we propose a new attention module, that we call Permutohedral Attention Module (PAM), to efficiently capture non-local characteristics of the image.  ...  This hyperplane is partitioned in simplices by a mesh called the Permutohedral Lattice. The Splat phase describes the vertices of the Permutohedral Lattice based on the neighbouring variables.  ... 
doi:10.1007/978-3-030-32226-7_44 fatcat:fml7rw32wzdvrc5g522gbodxiq

Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice [article]

Anne S. Wannenwetsch, Martin Kiefel, Peter V. Gehler, Stefan Roth
2019 arXiv   pre-print
Our proposed network layer builds on the permutohedral lattice, which performs sparse convolutions in a high-dimensional space allowing for powerful non-local operations despite small filters.  ...  Multiple features with different characteristics span this permutohedral space.  ...  Permutohedral Lattice. Adams et al . [1] propose the permutohedral lattice as a fast method for high-dimensional Gaussian filtering.  ... 
arXiv:1909.03677v1 fatcat:jd2mieu7vzba5mtdezvotwa45m

Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution

Longquan Dai, Liang Tang, Yuan Xie, Jinhui Tang
2018 Neural Information Processing Systems  
The permutohedral lattice operates uses the permutohedral lattice. Barycentric weights within each simplex are used to resample into and out of the lattice.  ...  The permutohedral lattice [Adams et al., 2010] is a sparse lattice that tessellates the space with simplices.  ... 
dblp:conf/nips/DaiT0T18 fatcat:szrww3i5bzgmxftlbzw2yoi2bm

Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients [article]

Kaidong Li, Ziming Zhang, Cuncong Zhong, Guanghui Wang
2022 arXiv   pre-print
We further propose an effective and efficient instantiation of our approach, namely, Lattice Point Classifier (LPC), based on structured sparse coding in the permutohedral lattice and 2D convolutional  ...  neural networks (CNNs) that is end-to-end trainable.  ...  Permutohedral lattice. Permutohedral lattice is a powerful operation to project the coordinates from a high dimensional space onto a hyperplane that defines the lattice.  ... 
arXiv:2203.15245v1 fatcat:xpkvb6nrwrahvbtplicvpf3klu

HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-Scale Point Clouds

Xiuye Gu, Yijie Wang, Chongruo Wu, Yong Jae Lee, Panqu Wang
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Operating on discrete and sparse permutohedral lattice points, our architectural design is parsimonious in computational cost.  ...  Permutohedral lattice. The integer lattice works fine in low-dimensional spaces.  ...  Our network first interpolates signals from the input points onto a permutohedral lattice.  ... 
doi:10.1109/cvpr.2019.00337 dblp:conf/cvpr/GuWWLW19 fatcat:dmurypc5r5fknkoslwlqdskxci

LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices [article]

Radu Alexandru Rosu, Peer Schütt, Jan Quenzel, Sven Behnke
2020 arXiv   pre-print
A PointNet describes the local geometry which we embed into a sparse permutohedral lattice. The lattice allows for fast convolutions while keeping a low memory footprint.  ...  Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images.  ...  To address these limitations, we propose four new operators on the permutohedral lattice which are more suitable for CNNs and dense prediction tasks.  ... 
arXiv:1912.05905v3 fatcat:ityumajvenfuhmozyfyq66cr2e

Video Propagation Networks [article]

Varun Jampani and Raghudeep Gadde and Peter V. Gehler
2017 arXiv   pre-print
Acknowledgments We thank Vibhav Vineet for providing the trained image segmentation CNN models for CamVid dataset.  ...  See Fig. 2 for a illustration of 2D permutohedral lattices.  ...  This was circumvented by a special data structure, the permutohedral lattice as proposed in [2] .  ... 
arXiv:1612.05478v3 fatcat:dpm7n3vomncdfglbftijlwggm4

Convolutional CRFs for Semantic Segmentation [article]

Marvin T. T. Teichmann, Roberto Cipolla
2018 arXiv   pre-print
challenging semantic image segmentation task the most efficient models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs  ...  The permutohedral lattice however is based on a complex data structure.  ...  In addition, efficient gradient computation of the permutohedral lattice approximation, is also a non-trivial problem.  ... 
arXiv:1805.04777v2 fatcat:pgwp2vx32zejhfyiyhk4zkfrda

Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation [article]

Miguel Monteiro, Mário A. T. Figueiredo, Arlindo L. Oliveira
2018 arXiv   pre-print
The available implementation of the permutohedral lattice was designed for 2D RGB images and only used CPU kernels 1 .  ...  The permutohedral lattice is a fast approximation of high dimensional filters which can be used for Gaussian and bilateral filtering.  ... 
arXiv:1807.07464v1 fatcat:7ljnxhhzfbffzadhjtqwgie7si

Video Propagation Networks

Varun Jampani, Raghudeep Gadde, Peter V. Gehler
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Acknowledgments We thank Vibhav Vineet for providing the trained image segmentation CNN models for CamVid dataset.  ...  See Fig. 2 for a illustration of 2D permutohedral lattices.  ...  This was circumvented by a special data structure, the permutohedral lattice as proposed in [2] .  ... 
doi:10.1109/cvpr.2017.336 dblp:conf/cvpr/JampaniGG17 fatcat:6owhveu7t5futbf3s4rpovcasq

SPLATNet: Sparse Lattice Networks for Point Cloud Processing [article]

Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz
2018 arXiv   pre-print
Naively applying convolutions on this lattice scales poorly, both in terms of memory and computational cost, as the size of the lattice increases.  ...  These layers maintain efficiency by using indexing structures to apply convolutions only on occupied parts of the lattice, and allow flexible specifications of the lattice structure enabling hierarchical  ...  Our approach generalizes this idea to highdimensional permutohedral lattice convolutions.  ... 
arXiv:1802.08275v4 fatcat:tbyf2nnm4jf55lbdsy5gz4l5oa

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

Radu Alexandru Rosu, Peer Schütt, Jan Quenzel, Sven Behnke
2021 arXiv   pre-print
A PointNet describes the local geometry which we embed into a sparse permutohedral lattice. The lattice allows for fast convolutions while keeping a low memory footprint.  ...  Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images.  ...  To address these limitations, we propose four new operators on the permutohedral lattice which are more suitable for CNNs and dense prediction tasks.  ... 
arXiv:2108.03917v1 fatcat:mvmekghirjejbda7sqanwx6jwa
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