The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.
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Updated
Sep 17, 2020 - C++
The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.
This repository provides implementation of an incremental k-d tree for robotic applications.
Fast algorithms to compute an approximation of the minimal volume oriented bounding box of a point cloud in 3D.
Benchmark of various spatial data structures for collision detection.
Open source library for scientific HPC
Unreal Engine Plugin: k-d tree
N body gravity attraction problem solver
libkdtree++ is an STL-like C++ template container implementation of k-dimensional space sorting, using a kd-tree. It sports a theoretically unlimited number of dimensions, and can store any data structure
A header-only C++ library for k nearest neighbor search with Eigen3.
Header-Only Collection of Clustering Algorithms for C++
vectorization of the kd-tree data structure and search algorithm
A C++ header only library for fast nearest neighbor and range searches using a KdTree. It supports interfacing with Eigen, OpenCV, and custom data types and provides optional Python bindings.
C++ implementation of KDTree & kNN classification on MNIST
Query-Aware LSH for Approximate NNS (PVLDB 2015 and VLDBJ 2017)
Intelligent monitoring of escalator.Function including traffic statistics,passenger retention detection and large object retention detection in escalator floor board. As well as human keypoints extraction and tracking in elevator.
Process LIDAR point cloud data for object detection. Implements RANSAC and Euclidean clustering with KD-Tree
Obstacle detection using lidar point cloud
Craig Reynolds' Boids model for simulating the flocking behavior of birds.
Query-Aware LSH for Approximate NNS (In-Memory Version of QALSH)
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