17 skills found
USTCPCS / CVPR2018 AttentionContext Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark
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ICWallis / FractoolboxPython tools for structural geology and borehole image analysis which includes data handling, frequency and geometric analysis, and reservoir geomechanics.
liborty / RstatsStatistics, Information Measures, Linear Algebra, Cholesky Matrix Decomposition, Mahalanobis Distance, Householder QR Decomposition, Clifford Algebra, Multidimensional Data Analysis, Geometric Median, Hulls, Machine Learning, multithreading implementation...
morphomatics / MorphomaticsGeometric morphometrics in non-Euclidean shape spaces.
corybrunson / Ordrmanage ordinations and render biplots in a tidyverse workflow
wangru25 / HERMESHERMES: A software package for simultaneous topological data analysis (persistent Betti numbers) and geometric data analysis (persistent eigenvalues).
matthew-hirn / Geo Scattering Graph DataCode repository for the paper "Geometric Scattering for Graph Data Analysis"
primitivefinance / Topological Data AnalysisThis repository is for topologic and geometric data analysis.
sjkoelle / MontlakeMontlake contains tools for geometric data analysis. It includes vector field group lasso and basis pursuit methods for parametric manifold learning. It also contains differentiable shape featurizations including interpoint distances, planar angles, and torsions from data positions for shape space analysis.
nicolas-robette / GDAtoolsR package for geometric data analysis and more
charalambos / P2C ClusteringClustering module in IEEE PAMI 2013: A Framework for Automatic Modeling from Point Cloud Data. A robust unsupervised clustering algorithm P2C, based on a hierarchical statistical analysis of the geometric properties of the data.
white-research / ShapesA Python package for analysis of geometric morphometric data.
lambdaxymox / Stanford Cs233 Spring 2015 2016CS233: Geometric And Topological Data Analysis Spring 2016.
LACourtenay / GraphGMMAn extension to geometric morphometric libraries oriented towards the integration of Graph Theory into the analysis of landmark data. Graph-based Geometric Morphometrics is additionally accompanied by other non-linear dimensionality reduction algorithms.
geogismx / LiDAR PointCloud ToolBoxThis toolbox uses spatial grid index to organize LIDAR point cloud data, with model builder and Python script building space model, and finally implementing a progressive morphological filtering algorithm for discrete point cloud processing; This toolbox also contains a new topographical feature point extraction method with a combination of hydrology water analysis and TIN geometric analysis.
edalford11 / ImageForensicsThis program is written in C++ and will read in a RAW image file that will perform MD5 and SHA1 checksums on the file before analysis. The program will then read the partition tables from the master boot record and extract the partition type, start sector address and the size of each partition in decimal. If the partition is of type FAT 16 or FAT 32, the program will locate the partitions volume boot record and extract the geometric data of the file system. This includes the start sector, end sector and size for the reserved area, sectors per cluster, start sector and ending sector of the FAT area, number of FATs, size of each FAT and the first sector of cluster 2.
