Search Results for "3d linux" - Page 4
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Showing 199 open source projects for "3d linux"
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Aphantasia
CLIP + FFT/DWT/RGB = text to image/video
This is a collection of text-to-image tools, evolved from the artwork of the same name. Based on CLIP model and Lucent library, with FFT/DWT/RGB parameterizes (no-GAN generation). Illustrip (text-to-video with motion and depth) is added. DWT (wavelets) parameterization is added. Check also colabs below, with VQGAN and SIREN+FFM generators. Tested on Python 3.7 with PyTorch 1.7.1 or 1.8. Generating massive detailed textures, a la deepdream, fullHD/4K resolutions and above, various CLIP models... -
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hloc
Visual localization made easy with hloc
This is hloc, a modular toolbox for state-of-the-art 6-DoF visual localization. It implements Hierarchical Localization, leveraging image retrieval and feature matching, and is fast, accurate, and scalable. This codebase won the indoor/outdoor localization challenges at CVPR 2020 and ECCV 2020, in combination with SuperGlue, our graph neural network for feature matching. We provide step-by-step guides to localize with Aachen, InLoc, and to generate reference poses for your own data using... -
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daily-paper-computer-vision
Document papers compiled daily in computer vision/deep learning
This repo is a running feed of computer-vision research, tracking new papers and notable results so practitioners can keep up without scouring multiple sites. It’s organized chronologically and often thematically, making it easy to scan what’s new in detection, segmentation, recognition, generative vision, 3D, and video understanding. The cadence is intentionally frequent, reflecting how quickly CV advances and how hard it is to maintain awareness while working full time. By aggregating... -
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AB3DMOT
Official Python Implementation for "3D Multi-Object Tracking
AB3DMOT is a real-time 3D multi-object tracking framework designed for applications such as autonomous driving and robotics perception. The system processes detection results from 3D object detectors that analyze LiDAR point clouds and uses them to track multiple objects across consecutive frames. Its tracking pipeline relies on a combination of classical algorithms, including a Kalman filter for state estimation and the Hungarian algorithm for data association between detected objects and... -
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Recast Navigation
Navigation mesh generation and pathfinding toolkit for game AI systems
Recast Navigation is an open source toolkit designed for building navigation meshes and performing pathfinding in 3D environments, particularly for video games and simulations. It provides a set of libraries that transform input geometry into navigation meshes that AI agents can use to move through virtual worlds. Its core component, Recast, constructs navigation meshes by rasterizing triangle meshes into voxels, filtering out areas that are not walkable, and converting the remaining regions... -
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Stable-Dreamfusion
Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion
A pytorch implementation of the text-to-3D model Dreamfusion, powered by the Stable Diffusion text-to-2D model. This project is a work-in-progress and contains lots of differences from the paper. The current generation quality cannot match the results from the original paper, and many prompts still fail badly! Since the Imagen model is not publicly available, we use Stable Diffusion to replace it (implementation from diffusers). Different from Imagen, Stable-Diffusion is a latent diffusion... -
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PIFuHD
High-Resolution 3D Human Digitization from A Single Image
PIFuHD (Pixel-Aligned Implicit Function for 3D human reconstruction at high resolution) is a method and codebase to reconstruct high-fidelity 3D human meshes from a single image. It extends prior PIFu work by increasing resolution and detail, enabling fine geometry in cloth folds, hair, and subtle surface features. The method operates by learning an implicit occupancy / surface function conditioned on the image and camera projection; at inference time it queries dense points to reconstruct a... -
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DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to... -
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Amiga Memories
A walk along memory lane
Amiga Memories is a project (started & released in 2013) that aims to make video programmes that can be published on the internet. The images and sound produced by Amiga Memories are 100% automatically generated. The generator itself is implemented in Squirrel, the 3D rendering is done on GameStart 3D. An Amiga Memories video is mostly based on a narrative. The purpose of the script is to define the spoken and written content. The spoken text will be read by a voice synthesizer (Text To... -
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Point-E
Point cloud diffusion for 3D model synthesis
point-e is the official repository for Point-E, a generative model developed by OpenAI that produces 3D point clouds from textual (or image) prompts. Its principal advantage is speed: it can generate 3D assets in just 1–2 minutes on a single GPU, which is significantly faster than many competing text-to-3D models. The model works via a two-stage diffusion approach: first, it uses a text → image diffusion network to produce a synthetic 2D view consistent with the prompt; then a second... -
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FrankMocap
A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
FrankMocap is a monocular 3D human capture system that estimates body, hand, and optionally face pose from a single RGB image or video. It regresses parametric human models (e.g., SMPL/SMPL-X) directly, producing temporally stable meshes and joint angles suitable for animation or analytics. The pipeline couples a robust 2D keypoint detector with 3D mesh regression networks and priors that keep results anatomically plausible. It can run frame-by-frame or with temporal smoothing, and includes... -
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Gym
Toolkit for developing and comparing reinforcement learning algorithms
Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no... -
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BEVFormer
Implementation of BEVFormer, a camera-only framework
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV... -
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Menagerie
A collection of high-quality models for the MuJoCo physics engine
MuJoCo Menagerie, developed by Google DeepMind, is a curated collection of high-quality simulation models designed for use with the MuJoCo physics engine. It serves as a comprehensive library of accurate and ready-to-use robotic, biomechanical, and mechanical models, ensuring users can perform reliable simulations without having to build or tune models from scratch. The repository aims to improve reproducibility and quality across robotics research by providing verified models that adhere to... -
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pyntcloud
pyntcloud is a Python library for working with 3D point clouds
This page will introduce the general concept of point clouds and illustrate the capabilities of pyntcloud as a point cloud processing tool. Point clouds are one of the most relevant entities for representing three dimensional data these days, along with polygonal meshes (which are just a special case of point clouds with connectivity graph attached). In its simplest form, a point cloud is a set of points in a cartesian coordinate system. Accurate 3D point clouds can nowadays be (easily and... -
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3D-Machine-Learning
A resource repository for 3D machine learning
3D-Machine-Learning is an open-source repository that compiles resources related to machine learning techniques applied to three-dimensional data. The project acts as a curated research directory that includes papers, datasets, tutorials, and software tools relevant to the emerging field of 3D machine learning. This interdisciplinary domain combines ideas from computer vision, computer graphics, and deep learning to analyze and generate three-dimensional structures. The repository includes... -
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Disco Diffusion
Notebooks, models and techniques for the generation of AI Art
A frankensteinian amalgamation of notebooks, models, and techniques for the generation of AI art and animations. This project uses a special conversion tool to convert the Python files into notebooks for easier development. What this means is you do not have to touch the notebook directly to make changes to it. The tool being used is called Colab-Convert. Initial QoL improvements added, including user-friendly UI, settings+prompt saving, and improved google drive folder organization. Now... -
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CIPS-3D
3D-aware GANs based on NeRF (arXiv)
3D-aware GANs based on NeRF (arXiv). This repository contains the code of the paper, CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis. The problem of mirror symmetry refers to the sudden change of the direction of the bangs near the yaw angle of pi/2. We propose to use an auxiliary discriminator to solve this problem. Note that in the initial stage of training, the auxiliary discriminator must dominate the generator more than the main discriminator... -
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AI Atelier
Based on the Disco Diffusion, version of the AI art creation software
Based on the Disco Diffusion, we have developed a Chinese & English version of the AI art creation software "AI Atelier". We offer both Text-To-Image models (Disco Diffusion and VQGAN+CLIP) and Text-To-Text (GPT-J-6B and GPT-NEOX-20B) as options. Making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. When a modified version is used to provide a... -
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MeshCNN in PyTorch
Convolutional Neural Network for 3D meshes in PyTorch
MeshCNN is a deep learning framework designed specifically for processing 3D triangular mesh data using convolutional neural networks. Unlike traditional CNNs that operate on images or voxel grids, MeshCNN performs convolution operations directly on the edges of mesh structures. This design allows the model to capture geometric relationships between mesh elements while preserving the underlying topology of 3D shapes. The framework introduces specialized layers such as edge-based convolution,... -
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Pathfinder MineSweeper
Une implémentation de l'algo A*
Ce programme est un pathfinder mettant en scène un robot démineur dont le comportement est simulé. Ici le programme extrait les données d'un terrain à partir d'une heightmap. Puis le robot simulé trace son chemin de bombe en bombe sur le terrain. Tout ceci vu par l'utilisateur via un affichage 3D. L'algorithme utilisé pour le pathfinding est l'algorithme A* (A STAR) -
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VRN
Code for "Large Pose 3D Face Reconstruction
The VRN (Volumetric Regression Network) repository implements the “Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression” method. Instead of explicitly fitting a 3D model via landmark estimation and deformation, VRN treats the reconstruction task as volumetric segmentation: it learns a CNN to regress a 3D volume aligned to the input image, and then extracts a mesh via isosurface from that volume. The network is unguided (no 2D landmarks as intermediate).... -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
Torch Points 3D is a framework for developing and testing common deep learning models to solve tasks related to unstructured 3D spatial data i.e. Point Clouds. The framework currently integrates some of the best-published architectures and it integrates the most common public datasets for ease of reproducibility. It heavily relies on Pytorch Geometric and Facebook Hydra library thanks for the great work! We aim to build a tool that can be used for benchmarking SOTA models, while also... -
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DensePose
A real-time approach for mapping all human pixels of 2D RGB images
DensePose is a computer vision system that maps all human pixels in an RGB image to the 3D surface of a human body model. It extends human pose estimation from predicting joint keypoints to providing dense correspondences between 2D images and a canonical 3D mesh (such as the SMPL model). This enables detailed understanding of human shape, motion, and surface appearance directly from images or videos. The repository includes the DensePose network architecture, training code, pretrained... -
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gym-pybullet-drones
PyBullet Gymnasium environments for multi-agent reinforcement
Gym-PyBullet-Drones is an open-source Gym-compatible environment for training and evaluating reinforcement learning agents on drone control and swarm robotics tasks. It leverages the PyBullet physics engine to simulate quadrotors and provides a platform for studying control, navigation, and coordination of single and multiple drones in 3D space.
