Research & Education

Animatable_nerf is an open-source research tool that provides the implementation for "Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos," a paper accepted to TPAMI 2024 and ICCV 2021. This tool allows researchers to generate realistic avatars from video footage by leveraging animatable neural fields. It supports various configurations, including vanilla Animatable NeRF, versions with neural blend weight fields replaced by displacement fields, and versions where the canonical NeRF model is replaced with a neural surface field (SDF output). The repository includes evaluation frameworks for reconstruction quality comparison and provides access to datasets like Mobile-Stage and SyntheticHuman++ for further research and development in neural rendering and 3D human body modeling.

GigaBrain is an AI-powered search platform designed to tap into the collective wisdom of online communities such as Reddit and YouTube. It works by scanning and analyzing billions of comments and discussions across these platforms to find authentic answers to user queries. The tool then compiles these relevant answers into a concise format, aiming to save users significant time and effort in gathering information. A 'PRO' version, GigaBrain PRO, is mentioned to offer access to more powerful AI models, suggesting enhanced capabilities or broader search scope.

ChangeMamba is an open-source tool designed for remote sensing change detection, leveraging a spatio-temporal state space model. It provides robust capabilities for various change detection tasks, including binary change detection (MambaBCD), semantic change detection (MambaSCD), and building damage assessment (MambaBDA). The tool is particularly useful for researchers and scientists working with Earth observation data, facilitating the analysis of changes in land cover and environmental conditions. It offers pre-trained weights for different model sizes (Tiny, Small, Base) and supports training and inference on several benchmark datasets like SYSU, LEVIR-CD+, WHU-CD, SECOND, and xBD. The project is actively maintained with regular updates and has been recognized in IEEE TGRS.

nnDetection is a self-configuring framework designed for 3D (volumetric) medical object detection, addressing the challenge of cumbersome method configuration in medical image analysis. Following the success of nnU-Net for image segmentation, nnDetection systematizes and automates the configuration process, allowing it to adapt to arbitrary medical detection problems without manual intervention. It achieves results comparable to or superior to state-of-the-art methods. The framework includes guides for 12 datasets used in its development and evaluation, such as ADAM and LUNA16, and supports easy integration of new datasets through a standardized input format. It is built with Python 3.8+, PyTorch, and uses Docker for easy deployment.

PointRCNN is an open-source 3D object detector that directly generates accurate 3D box proposals from raw point cloud data in a bottom-up manner. It then refines these proposals using a bin-based 3D box regression loss. This tool was the first two-stage 3D object detector to use only raw point cloud as input, achieving state-of-the-art performance on the KITTI dataset at the time of its submission. PointRCNN supports features like multiple GPUs for training, GPU version rotated NMS, and faster PointNet++ inference and training. It is implemented in Python with PyTorch 1.0 and TensorboardX, making it suitable for researchers and developers in autonomous systems and computer vision.

NTIRE2017 is an open-source project offering a Torch implementation of "Enhanced Deep Residual Networks for Single Image Super-Resolution." Developed by Team SNU_CVLab, it was recognized with the Best Paper Award at the CVPR 2017 Workshop (2nd NTIRE). The repository includes detailed model architectures (EDSR, MDSR), NTIRE2017 Super-resolution Challenge results, and demo and training code. Users can access trained models, information on datasets like DIV2K and Flickr2K, and super-resolution examples. The code is based on Facebook's Torch implementation of ResNet and also provides a PyTorch version for some models. It's designed for researchers and developers working on image restoration and enhancement, particularly in the field of single image super-resolution.

drl-zh, or "Deep Reinforcement Learning: Zero to Hero!", offers a comprehensive and hands-on course designed to teach deep reinforcement learning. The curriculum is divided into two main parts: foundational concepts, where users build algorithms like DQN, SAC, and PPO from scratch, and advanced topics, which delve into areas such as curiosity-driven exploration, AlphaZero, and Reinforcement Learning with Human Feedback (RLHF). The course emphasizes learning by doing, with practical exercises ranging from playing Atari games and training robots to fine-tuning Language Models and implementing self-play with MCTS. It's structured around interactive Jupyter notebooks, providing guided TODO sections and complete solutions for reference. The entire experience is optimized for a VS Code environment, with a Dockerized setup for quick and reproducible development.

efficientdet is a PyTorch implementation of the EfficientDet object detection model, developed by Signatrix GmbH. This open-source tool provides scalable and efficient object detection capabilities, making it suitable for various computer vision tasks. It includes pre-trained weights, allowing users to get started quickly without extensive training. The repository offers scripts for training models, evaluating mean average precision (mAP) on datasets like COCO, and testing models on both datasets and video inputs. It supports Python 3.6 and PyTorch 1.2, along with other common libraries like OpenCV and TensorBoard. The implementation borrows concepts from RetinaNet, providing a robust framework for object detection research and application.

FAST-LIVO2 is an efficient and accurate open-source LiDAR-inertial-visual fusion localization and mapping system. It is designed for real-time 3D reconstruction and onboard robotic localization, particularly in severely degraded environments. The system integrates data from LiDAR, inertial measurement units, and visual sensors to provide robust odometry. Key features include its direct fusion approach, support for resource-constrained platforms, and an associated dataset for evaluation. The project also provides resources for building a hard-synchronized handheld device, including CAD files and source code, making it a comprehensive solution for developers working on autonomous navigation and robotics.

Conference-Accepted-Paper-List is a GitHub repository designed to centralize information on accepted papers from a variety of conferences within the fields of artificial intelligence, machine learning, and robotics. The repository provides convenient, quick links directly to conference submission notifications and the lists of accepted papers. While serving as a useful starting point for researchers and academics, it also recommends leveraging more comprehensive academic search engines like dblp and Aminer for in-depth research and broader searches.

WildGS-SLAM is an open-source research tool designed for monocular Gaussian Splatting SLAM in dynamic environments. Developed for Computer Vision and Pattern Recognition (CVPR) 2025, it excels at accurately tracking camera trajectories and reconstructing 3D Gaussian maps for static elements from monocular video sequences, even when captured in the wild with dynamic distractors. The tool effectively removes all dynamic components to provide a clear static reconstruction. It supports various datasets including Wild-SLAM Mocap, Wild-SLAM iPhone, Bonn Dynamic, and TUM RGB-D, and also allows users to integrate their own custom datasets. WildGS-SLAM provides functionalities for camera pose evaluation and novel view synthesis, making it a valuable resource for researchers in the field.

whatlanguage is a Ruby library designed for efficient text language detection. It leverages bloom filters to achieve high speed and memory efficiency, making it suitable for processing larger text blocks like blog posts or comments. The library supports a wide array of languages including Dutch, English, Farsi, French, German, Italian, Pinyin, Swedish, Portuguese, Russian, Arabic, Finnish, Greek, Hebrew, Hungarian, Korean, Norwegian, Polish, and Spanish. While effective for longer texts, it is noted to perform poorly on very short or Twitter-esque content. The project, initially built in 2007, has received minor updates to ensure compatibility with modern Ruby implementations, though the core algorithms remain largely unchanged.

f2-nerf is an open-source project designed for fast neural radiance field (NeRF) training, specifically optimized for scenarios involving free camera trajectories. Built primarily on LibTorch, this tool provides a robust framework for efficient 3D scene reconstruction and novel view synthesis. Users can train F2-NeRF on custom data, including images processed with COLMAP or hloc, and generate camera poses. It also includes scripts for rendering test images and creating render paths by interpolating input camera poses. The project leverages several powerful libraries such as tiny-cuda-nn for fast MLP training, happly for PLY I/O, and eigen for linear algebra, making it a comprehensive solution for advanced NeRF applications.

FSGS, short for "Real-Time Few-Shot View Synthesis using Gaussian Splatting," is an advanced AI tool presented at ECCV 2024. It specializes in generating new views of a scene from a minimal number of input images, leveraging Gaussian Splatting technology for real-time performance. The tool provides comprehensive environmental setups, including Conda package management and CUDA 11.7 support, ensuring a robust development environment. Users can prepare data by reconstructing sparse view inputs using SfM and dense stereo matching with COLMAP, supporting datasets like LLFF and MipNeRF-360. FSGS offers clear instructions for training models with varying view counts, rendering images, and evaluating model performance, making it a valuable resource for researchers and developers in computer vision and graphics.

InfoShelves Workday Certification app is an AI-driven platform designed to help professionals prepare for Workday certifications. It offers authentic certification practice tests and study notes across various Workday areas, including HCM Pro, Financials, Reporting, and Integration. The platform aims to provide a focused and efficient way to study and pass Workday certification exams, boosting career prospects. With AI-driven discovery, users can master Workday concepts and prepare effectively for their professional development. The app focuses on providing comprehensive exam simulators to ensure users are well-prepared for their certification journey.

SummarizeThis.io is a tool built to provide concise summaries of diverse content formats. Users can input text, web page URLs, PDF documents, or YouTube video links, and the tool will generate a condensed version of the information. This functionality is particularly useful for quickly grasping the main points of lengthy articles, reports, or video content without needing to consume the entire source. It aims to save time and improve information digestion.

visual_anagrams is an open-source tool specifically designed for generating multi-view optical illusions. It leverages advanced diffusion models to create these unique visual effects. The tool offers readily available code, making it accessible for hands-on experimentation. It also includes Colab notebooks, catering to both free and Pro tier users, to facilitate the creation of visual anagrams and exploration of factorized diffusion techniques. This makes it a valuable resource for those interested in the intersection of AI and visual art.

Genesis is a physics platform designed for general-purpose Robotics, Embodied AI, and Physical AI applications. It functions as a universal physics engine rebuilt from the ground up, capable of simulating a wide range of materials and physical phenomena. The platform is lightweight, ultra-fast, pythonic, and user-friendly, offering a powerful photo-realistic rendering system. Genesis also acts as a generative data engine, transforming natural language descriptions into various data modalities. It aims to lower the barrier to using physics simulations, unify diverse physics solvers, and automate data generation for robotics research and development.

pointnerf is an open-source implementation of Point-NeRF, a method for modeling radiance fields using neural 3D point clouds with associated neural features. This tool enables efficient rendering by aggregating neural point features near scene surfaces through a ray marching-based pipeline. A key differentiator is its ability to be initialized via direct inference of a pre-trained deep network to produce a neural point cloud, which can then be finetuned for visual quality surpassing NeRF with significantly faster training times. pointnerf also integrates with other 3D reconstruction methods and manages errors and outliers through a novel pruning and growing mechanism, making it suitable for various research applications in computer vision and graphics.

LuaRadio is a lightweight and embeddable flow graph signal processing framework specifically designed for software-defined radio (SDR). Built on LuaJIT, it offers a small binary footprint and no external hard dependencies, making it highly portable. The framework provides a comprehensive suite of source, sink, and processing blocks, along with a simple API for defining and running flow graphs, creating custom blocks, and managing data types. It's ideal for rapidly prototyping software radios, developing modulation/demodulation utilities, and conducting signal processing experiments. LuaRadio can also be embedded into existing radio applications, serving as a user-scriptable engine for advanced signal processing tasks. It supports computational acceleration through LuaJIT's FFI to wrap external libraries like VOLK, liquid-dsp, and others, ensuring efficient performance.

FSA-Net is an open-source research tool designed for head pose estimation from a single image, developed by Tsun-Yi Yang. Published at CVPR19, it introduces a novel approach based on regression and fine-grained feature aggregation. Unlike previous methods that often rely on landmark or depth estimation, FSA-Net aims for a more compact model by employing a soft stagewise regression scheme. A key innovation is its ability to learn fine-grained structure mapping to spatially group features before aggregation, providing part-based information and pooled values. The tool supports various face detectors like LBP, MTCNN, and SSD for robust and fast performance. It is implemented in Keras and TensorFlow, making it accessible for researchers and developers in computer vision and facial analysis.

llm-twin-course is a free educational resource designed to guide users through the process of building a production-ready Large Language Model (LLM) and Retrieval Augmented Generation (RAG) system. The course emphasizes LLMOps best practices, offering practical, hands-on lessons and accompanying source code. It covers the entire development lifecycle, from initial data gathering to the final stages of productionizing LLMs, with a specific focus on creating an AI replica.

Tavily is a specialized search engine designed to enhance Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. Its primary function is to deliver efficient and rapid search results, connecting LLMs and other AI applications to real-time knowledge. By providing access to trusted information, Tavily aims to mitigate issues like hallucinations and bias in AI outputs. The platform offers capabilities such as real-time search, information extraction, research assistance, and web crawling, all accessible via an API to help AI make more informed decisions.

SC-GS provides code for Sparse-Controlled Gaussian Splatting, designed for editable dynamic scenes. This open-source tool allows users to effortlessly edit and customize their digital assets through interactive features. It represents motion using sparse control points, which drive 3D Gaussians for high-fidelity rendering. The approach supports both dynamic view synthesis and motion editing, making it versatile for various applications. Recent updates include support for editing static Gaussians from .ply files, improved handling of real-world static objects, and video rendering with interpolation of editing results. It offers two ARAP deformation strategies for motion editing: iterative deformation and deformation from Laplacian initialization, giving users flexibility in achieving desired effects.