48 skills found · Page 1 of 2
Habrador / Unity Programming PatternsImplementations of programming design patterns in Unity with examples in C# when to use them.
lohedges / AabbccDynamic AABB trees in C++ with support for periodic systems.
Habrador / Ten Minute Physics UnityImplementations in Unity of the Ten Minute Physics YouTube channel. Instead of using Unity's built-in physics engine, you will learn how to make your own XPBD based physics engine. This is useful if you want to simulate ropes, cloth, tires, etc. You will also learn how to make fluid simulations and soft body physics.
milibopp / AcaciaA spatial partitioning and tree library in Rust.
LMissher / PatchSTG[SIGKDD'2025] Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective
ErikSom / Threejs OctreeLightweight and efficient spatial partitioning lib designed specifically for Three.js
Erfan-Ahmadi / CircleCollisionImplementing Different Methods of Circle to Circle Collision Detection using variety of new Technologies: Vulkan Graphics/Compute API, AVX2/AVX-512
terrybrash / Dragon SpaceSpatial partitioning concepts and implementions.
Nikorasu / PyNBoidsThis is a Boids Simulation, written in Python with Pygame.
loosegrid / DragonSpace DemoA simple boids simulation to show the difference between spatial partitioning structures
dolejska-daniel / Unity QuadtreeImplementation of generic spatial partitioning algorithm (Quadtree) for Unity.
Sopiro / DynamicBVHDynamic BVH visualization
Uriopass / Flat SpatialFlat spatial partitionning data structures in Rust
vittorioromeo / SSVSCollision[HEADER-ONLY] C++14 AABB simple collision detection/response framework for games. Depends on SSVStart, SFML2.0. It has nice performance. Features interchangeable spatial partitioning and resolution systems, a way to prevent the "crack problem", easy to use C++11 lambda callbacks for collision events. It's not perfect, but it should work very well for any simple 2D game.
ropensci / ChopinComputation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing http://doi.org/10.1016/j.softx.2025.102167
sayantann11 / Clustering Modelsfor MLlustering in Machine Learning Introduction to Clustering It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical. Such as : DBSCAN: Density-based Spatial Clustering of Applications with Noise These data points are clustered by using the basic concept that the data point lies within the given constraint from the cluster centre. Various distance methods and techniques are used for calculation of the outliers. Why Clustering ? Clustering is very much important as it determines the intrinsic grouping among the unlabeled data present. There are no criteria for a good clustering. It depends on the user, what is the criteria they may use which satisfy their need. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection). This algorithm must make some assumptions which constitute the similarity of points and each assumption make different and equally valid clusters. Clustering Methods : Density-Based Methods : These methods consider the clusters as the dense region having some similarity and different from the lower dense region of the space. These methods have good accuracy and ability to merge two clusters.Example DBSCAN (Density-Based Spatial Clustering of Applications with Noise) , OPTICS (Ordering Points to Identify Clustering Structure) etc. Hierarchical Based Methods : The clusters formed in this method forms a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category Agglomerative (bottom up approach) Divisive (top down approach) examples CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies) etc. Partitioning Methods : These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter example K-means, CLARANS (Clustering Large Applications based upon Randomized Search) etc. Grid-based Methods : In this method the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operation done on these grids are fast and independent of the number of data objects example STING (Statistical Information Grid), wave cluster, CLIQUE (CLustering In Quest) etc. Clustering Algorithms : K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problem.K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster . Applications of Clustering in different fields Marketing : It can be used to characterize & discover customer segments for marketing purposes. Biology : It can be used for classification among different species of plants and animals. Libraries : It is used in clustering different books on the basis of topics and information. Insurance : It is used to acknowledge the customers, their policies and identifying the frauds. City Planning: It is used to make groups of houses and to study their values based on their geographical locations and other factors present. Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones. References : Wiki Hierarchical clustering Ijarcs matteucc analyticsvidhya knowm
shangsw / HPDM SPRNSpectral Partitioning Residual Network with Spatial Attention Mechanism for Hyperspectral Image Classification
PaulDemeulenaere / Vfx Neighborhood Grid 3dGPU-based flocking driven by VFXGraph using a compute shader for spatial partitioning
Amey-Thakur / QUADTREE VISUALIZERA high-performance interactive simulation visualizing the efficiency of the QuadTree data structure in spatial partitioning and collision detection, built with Next.js and HTML5 Canvas.
LDGerrits / QuickZoneA high-performance, physics-free spatial query library for Roblox. Maintain 60 FPS with 1M+ zones.