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global_l0

Global L0 algorithm for regularity-constrained plane fitting

gltf

glTF – Runtime 3D Asset Delivery

gpu-accelerated-point-cloud-registration-using-hierarchical-gmm

gpu-fluid

High performance OpenGL fluid solver in C++

gpu-gems-book-source-code

:cd: CD Content ( Source Code ) Collection of Book <GPU Gems > 1~ 3 | 《GPU精粹》 1~ 3 随书CD（源代码）珍藏

gpu-icp-slam

gpu-normal-computation

Performing normal computation for big point clouds on the gpu using openCL

gpu-voxels

GPU-Voxels is a CUDA based library which allows high resolution volumetric collision detection between animated 3D models and live pointclouds from 3D sensors of all kinds.

grid-gcn

Grid-GCN for Fast and Scalable Point Cloud Learning

gslam

A General Simultaneous Localization and Mapping Framework which supports feature based or direct method and different sensors including monocular camera, RGB-D sensors or any other input types can be handled.

gslicr

gSLICr: Real-time super-pixel segmentation

hierarchical-localization

Visual localization made easy with hloc

horizonnet

Pytorch implementation of HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation.

hypervid

开源移动端车型识别 Mobile Plateform Vehicle Identification Model

icpcuda

Super fast implementation of ICP in CUDA for compute capable devices 3.5 or higher

imguizmo

Immediate mode 3D gizmo for scene editing and other controls based on Dear Imgui

india_2018_districts

india_boundary_updated

For Details: https://bit.ly/2V0wGMq

india_official_boundaries_2019

india_pincodes

infinitam

A Framework for the Volumetric Integration of Depth Images

iterative-closest-point

itowns

A Three.js-based framework written in Javascript/WebGL for visualizing 3D geospatial data

jsenet

Implementation of ECCV2020 paper - JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds

kendzi3d

3D plugin for JOSM

kht

Kernel-Based Hough Transform for Detecting Straight Lines in Images

kicad-footprints

Official KiCad Footprint Libraries for Kicad version 5

kosovo_official_shapefile_data

kpconv

Kernel Point Convolutions

lane-marking-detection

This is the final project for the Geospatial Vision and Visualization class at Northwestern University. The goal of the project is detecting the lane marking for a small LIDAR point cloud. Therefore, we cannot use a Deep Learning algorithm that learns to identify the lane markings by looking at a vast amount of data. Instead we will need to build a system that is able to identify the marking just by looking at the intensity value within the point cloud.