areslp

matlab

LSTR

This is an official repository of End-to-end Lane Shape Prediction with Transformers.

SuperPoint

Efficient neural feature detector and descriptor

ChatGPT-Next-Web

A cross-platform ChatGPT/Gemini UI (Web / PWA / Linux / Win / MacOS). 一键拥有你自己的跨平台 ChatGPT/Gemini 应用。

ControlNet

Let us control diffusion models!

copilot_detection_coco

deep_learning

DiffSinger

DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism (SVS & TTS); AAAI 2022; Official code

FFD

Fast-Feature-Detector

Grounded-Segment-Anything

Marrying Grounding DINO with Segment Anything & Stable Diffusion & BLIP - Automatically Detect , Segment and Generate Anything with Image and Text Inputs

huggingface_tools

JARVIS

JARVIS, a system to connect LLMs with ML community

kinematic3d

LaneATT

Code of the paper entitled "Keep your Eyes on the Lane: Attention-guided Lane Detection"

LIO-SAM

LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

models

Models and examples built with TensorFlow

multinerf

A Code Release for Mip-NeRF 360, Ref-NeRF, and RawNeRF

OpenGlue

Open Source Graph Neural Net Based Pipeline for Image Matching

packnet-sfm

TRI-ML Monocular Depth Estimation Repository

PVIO

Robust and Efficient Visual-Inertial Odometry with Multi-plane Priors

pyslam

pySLAM contains a monocular Visual Odometry (VO) pipeline in Python. It supports many modern local features based on Deep Learning.

pytorch-superpoint

Superpoint Implemented in PyTorch: https://arxiv.org/abs/1712.07629

PyTorch-VAE

A Collection of Variational Autoencoders (VAE) in PyTorch.

RobustVideoMatting

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

SMERF

SuperGluePretrainedNetwork

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

test

vimrc

vits

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech