An unsupervised learning framework for depth and ego-motion estimation from monocular videos
Les fonctionnalités principales de tinghuiz/sfmlearner sont : Vision par ordinateur, Computer Vision and Image Processing, Geometry and Depth Estimation, Optical Flow and Depth.
Les alternatives open-source à tinghuiz/sfmlearner incluent : alicevision/meshroom — Meshroom is a node-based photogrammetry software designed to transform collections of two-dimensional images into… dbolya/yolact — Yolact is a computer vision framework and real-time instance segmentation model. It utilizes a fully convolutional… aleju/imgaug — imgaug is a Python library for machine learning data augmentation and computer vision dataset expansion. It provides… alicevision/alicevision — 3D Computer Vision Framework. balavenkatesh3322/cv-pretrained-model — A collection of computer vision pre-trained models. ermig1979/simd — C++ image processing and machine learning library with using of SIMD: SSE, AVX, AVX-512, AMX for x86/x64, NEON, SVE…
Meshroom is a node-based photogrammetry software designed to transform collections of two-dimensional images into three-dimensional models and scene geometry. It provides a visual interface for constructing and managing modular data pipelines, allowing users to automate complex computer vision tasks such as feature extraction, depth map estimation, and mesh generation. The software distinguishes itself through a distributed computational framework that dispatches resource-intensive tasks across local hardware or remote render farms. By utilizing a directed acyclic graph execution model, it en
imgaug is a Python library for machine learning data augmentation and computer vision dataset expansion. It provides tools to increase the volume and variety of training sets by applying random geometric, color, and noise transformations to images. The library ensures spatial consistency by synchronizing transformations across images and their associated annotations, such as bounding boxes, keypoints, and segmentation maps. It uses a compositional pipeline pattern to chain multiple augmentations into sequences and employs deterministic seed management to reproduce specific data samples. The
A collection of computer vision pre-trained models.