Open-source alternatives to Ceres Solver

Extensible Optimization Framework

GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.

g2o: A General Framework for Graph Optimization

This project is a numerical computing library designed for scientific and engineering mathematical operations. It functions as a comprehensive linear algebra framework, a statistical analysis library, and a toolkit for mathematical optimization and numerical integration. The library is distinguished by its provider-based native acceleration, which allows managed code to be swapped for platform-native binary libraries to increase the performance of computationally intensive routines. It also supports a hybrid approach to matrix storage, implementing separate strategies for dense and sparse mat

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

ArrayFire is a hardware-agnostic compute framework and JIT-compiled tensor engine designed for high-performance numerical computing. It serves as a GPU numerical computing library and parallel signal processing toolkit that abstracts hardware backends, allowing the same codebase to execute across various GPU architectures and CPUs. The project distinguishes itself through a JIT engine that uses expression compilation to fuse operations and minimize memory overhead. It employs a deferred execution graph to optimize computation chains and provides interoperability primitives to share data and e

Drake is a robotics simulation framework and control system modeling tool used for designing, simulating, and verifying the dynamics of complex robotic systems. It functions as a multibody dynamics simulator and a mathematical optimization library, providing a suite of algorithms for trajectory optimization and the simulation of articulated robots. The framework is distinguished by its block-diagram system for composing dynamical subsystems and its ability to formulate and solve diverse mathematical programs, including linear, quadratic, and nonconvex nonlinear problems. It supports specializ

cuml is a GPU-accelerated machine learning library and framework that uses CUDA to accelerate tabular data preprocessing and model execution. It provides a suite of tools for training and deploying classification, regression, and clustering models on NVIDIA GPUs and GPU clusters. The library is designed for scalability, offering a distributed GPU machine learning environment that can spread computation and data across multiple hardware accelerators and nodes to handle datasets exceeding single-device memory. It mirrors standard estimator interfaces to allow the replacement of CPU-based models

Optim.jl is a numerical optimization library for the Julia programming language, providing a comprehensive framework for minimizing or maximizing univariate and multivariate functions. It offers a suite of tools for solving both constrained and unconstrained mathematical problems, utilizing a variety of gradient-based, derivative-free, and stochastic search methods. The library distinguishes itself through a modular architecture that leverages language-level multiple dispatch to automatically select efficient solvers based on input data types and objective function properties. It supports com

Slambook is a visual SLAM framework designed for simultaneous localization and mapping. It provides an integrated system to estimate camera motion and reconstruct 3D environments using visual sensor data. The project includes a visual odometry engine to track camera movement and a dense 3D reconstruction tool for creating volumetric representations of scenes. It features a loop closure detection system to recognize previously visited locations and a pose graph optimizer to refine trajectories and ensure global map consistency. The framework covers spatial estimation and environment modeling

FinceptTerminal is a quantitative finance platform and financial engineering library designed for asset valuation, risk management, and fixed-income analytics. It provides a comprehensive suite for algorithmic trading and investment strategy automation, integrating specialized language model agents and node-based workflows to automate market research and alpha generation. The project distinguishes itself with a dedicated game theory analysis engine for calculating Nash equilibria and simulating strategic interactions in competitive markets. It also features a specialized credit risk modeling

VINS-Fusion is a multi-sensor fusion framework and visual-inertial odometry system. It integrates camera images, inertial measurement unit data, and global positioning signals through a non-linear optimization system to track the position and orientation of autonomous vehicles. The system includes a visual loop closure engine that utilizes a bag-of-words approach to recognize previously visited locations and correct trajectory drift. It further provides tools for online spatio-temporal calibration to determine the physical offset and time synchronization between cameras and inertial sensors d

Kalibr is a software suite for multi-camera calibration and visual-inertial parameter estimation. It provides a mathematical framework for determining intrinsic and extrinsic parameters across multiple cameras and calculating the spatio-temporal offsets between cameras and inertial measurement units. The project features a non-linear least-squares optimizer to minimize reprojection and inertial errors. It includes specialized tools for rolling-shutter camera calibration to estimate projection and distortion parameters for sensors that capture images row-by-row. The system covers a broad rang

COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion pipeline. It functions as a GPU-accelerated photogrammetry tool and multi-view stereo framework designed to produce dense 3D geometry and watertight meshes from collections of 2D images. The project distinguishes itself through hardware-accelerated feature extraction and a modular camera modeling system that supports perspective, fisheye, and equirectangular lens types. It employs vocabulary tree image retrieval to efficiently identify similar images in large datasets and provides P

openMVG is a computer vision geometry library and toolkit for multiple view geometry. It serves as a framework for structure from motion and 3D scene reconstruction, providing the tools necessary to recover 3D point clouds and camera poses from collections of 2D images. The library implements both global and incremental structure-from-motion pipelines. It uses geometric algorithms to calculate camera pose estimation and image localization, employing Levenberg-Marquardt bundle adjustment to refine 3D coordinates and camera parameters by minimizing reprojection error. The project covers a broa

ORB_SLAM3 is a visual-inertial SLAM library designed for real-time simultaneous localization and mapping. It provides a framework for tracking camera movement and building 3D maps of environments using monocular, stereo, or RGB-D cameras combined with inertial sensors. The system features a multi-map fusion engine capable of merging separate spatial sessions into a single seamless representation of an environment. It includes specialized processing for wide-angle and fisheye lenses to expand the visual field of view for spatial tracking. The library covers a broad range of spatial intelligen

This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ

Cartographer is a software library and spatial localization engine for simultaneous localization and mapping. It provides a framework for calculating the precise position and orientation of a device while concurrently generating real-time 2D and 3D representations of its environment using lidar-based data. The system implements a real-time mapping approach that uses live sensor streams to track device heading and position. It utilizes a submap-based mapping strategy to divide environments into local maps that are aligned into a global map. The project covers a range of SLAM capabilities, inc

LIO-SAM is a lidar inertial SLAM framework and tightly-coupled sensor fusion pipeline. It functions as a factor graph optimization engine that combines lidar scans and inertial measurement unit data to build 3D point cloud maps and estimate robot trajectories. The system integrates global position factors to align local coordinates with real-world data. It employs loop closure detection to identify previously visited locations, creating constraints in the optimization graph to correct accumulated global drift. The framework covers lidar inertial odometry, point cloud processing, and trajecto

Scanpy is a Python library for the preprocessing, visualization, and analysis of large-scale single-cell gene expression datasets. It serves as a toolkit for single-cell RNA sequencing analysis, providing a framework to process and analyze genomic data from individual cells to identify biological markers and cell types. The library includes a scalable data processing pipeline for cleaning and preparing genomic data, a clustering framework for grouping cells with similar expression profiles, and a system for modeling transitions between cell states to reconstruct biological development and dif

DeepLearningZeroToAll is a comprehensive educational resource and implementation collection focused on deep learning and machine learning. It provides a structured learning path using TensorFlow to move from foundational linear models to complex neural network architectures. The project is distinguished by its practical implementations of various network types, including multilayer perceptrons for logic problems, convolutional neural networks for spatial data and image recognition, and recurrent neural networks using LSTM cells for time-series forecasting and character sequence prediction. It

ORB_SLAM2 is a visual simultaneous localization and mapping system that tracks camera movement and builds 3D environments from image data. It functions as a real-time visual odometry tool and sparse 3D reconstructor, computing the position and orientation of a camera while generating a point cloud map of a physical space. The system utilizes a camera relocalization engine to identify a camera's position within a known map after tracking failure or system restarts. It incorporates a spatial tracker to enable the precise insertion and composition of virtual 3D objects into real-world planar reg

Flashlight is a C++ machine learning library and deep learning framework designed for building and training neural networks. It functions as a tensor manipulation library and an automatic differentiation engine that tracks operations to calculate gradients via backpropagation for model optimization. The project is distinguished by its role as a distributed training framework, utilizing all-reduce gradient synchronization and distributed environments to scale machine learning workloads across multiple nodes and devices. It features a backend-agnostic memory interface and RAII-based management

This project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow. It provides a practical codebase for both model-free and model-based learning agents, designed to demonstrate how AI agents learn through trial and error. The collection features detailed implementations of various algorithmic approaches, including Deep Q-Networks and Policy Gradient methods. It specifically covers Actor-Critic architectures for continuous and discrete action spaces, alongside Proximal Policy Optimization and Deep Deterministic Policy Gradients. The framewor

This project is a TensorFlow meta-learning framework and research toolkit designed to implement and train learned optimizers. It provides a library of tools for developing neural networks that learn how to optimize other models, replacing traditional gradient-based optimization algorithms. The framework includes a problem ensemble manager that allows multiple distinct optimization tasks to be combined into a single weighted loss function for simultaneous training. It uses a factory pattern for network instantiation and supports the definition of custom objective functions and loss graphs as t

This project is a machine learning educational resource and implementation guide for Python. It provides a collection of executable code and notebooks that demonstrate predictive modeling, data analysis workflows, and the implementation of various machine learning algorithms. The repository features practical examples of classification, regression, and clustering tasks using Scikit-Learn, alongside tutorials for building and training deep learning architectures with TensorFlow. These include implementations of convolutional and recurrent networks. The content covers a broad range of capabili