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it-net

Implementation of Iterative Transformer Network for 3D Point Cloud

iterative-closest-point

Implementation of the iterative closest point algorithm. A point cloud is transformed such that it best matches a reference point cloud.

iterative-closest-point-1

iterative-closest-point-icp---svd

Iterative Closest Point algorith implemantation to register 2 point clouds

itktrimmedpointsetregistration

itree3dmap

3D Mapping of a tree for dendrometric feature estimation

iv2018-hierarchical-semantic-segmentation-for-heterogeneous-datasets

Datasets and code for reproducing results for IV2018 paper "Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation".

iv2019-boosting-semantic-segmentation-with-weak-labels

Code and dataset for IV 2019 paper titled Boosting Semantic Segmentation with Weak Labels

jdata

京东JData算法大赛-高潜用户购买意向预测入门程序(starter code)

joint_point_based

jointnet-a-common-neural-network-for-road-and-building-extraction

Python implementation of Convolutional Neural Networks (CNNs) proposed in paper

jointseg

Joint Segmentation of 3D Meshes

js3c-net

Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion (AAAI 2021)

jsis3d

Joint Semantic-Instance Segmentation of 3D Point Clouds

jsnet

Code for paper JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds.

jule.torch

Torch code for our CVPR 2016 paper "Joint Unsupervised LEarning of Deep Representations and Image Clusters"

k-means-classification

A from scratch implementation of the K-Means Classification Algorithm to classify four categories of words based on three hundred feature vectors through unsupervised means

k-means-clustering

Please use you own data set. Here in this code I have used systems load vs cpu_percentage usage at a particular point of time.

k3d-jupyter

K3D lets you create 3D plots backed by WebGL with high-level API (surfaces, isosurfaces, voxels, mesh, cloud points, vtk objects, volume renderer, colormaps, etc). The primary aim of K3D-jupyter is to be easy for use as stand alone package like matplotlib, but also to allow interoperation with existing libraries as VTK.

k4pcs_-modified

K+4PCS is a efficient point cloud registration algrithm especially for large amount number without initial pose guess.

kabamaru

Point cloud (RGBD) C++ tools for registration, 3D SLAM, box detection/fitting, and more.

kaggle_digit_recognition

kailigo.github.io

kaolin

A PyTorch Library for Accelerating 3D Deep Learning Research

kcnet

Code for "Mining Point Cloud Local Structures by Kernel Correlation and Graph Max Pooling", CVPR, 2018.

kd_filter

use Kd-tree to filter point noise

kdnet-tensorflow

KdNet

kdnet.pytorch

implementation "Escape from Cells: Deep Kd-Networks for The Recognition of 3D Point Cloud Models" in pytorch

keras-segmentation

🕹基于keras的语义分割，fcn32/fcn8/SegNet/U-Net

keypoint-learning

Code for "Learning a Descriptor-Specific 3D Keypoint Detector" and "Learning to detect good 3d keypoints" -ICCV 2015, IJCV 2018