areslp's repositories

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LSTR

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

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SuperPoint

Efficient neural feature detector and descriptor

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camera_calibration

Accurate geometric camera calibration with generic camera models

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ControlNet

Let us control diffusion models!

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DiffSinger

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

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FasterSeg

[ICLR 2020] "FasterSeg: Searching for Faster Real-time Semantic Segmentation" by Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, Zhangyang Wang

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FFD

Fast-Feature-Detector

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Grounded-Segment-Anything

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

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IMUCalibration-Gesture

calibration for Imu and show gesture

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JARVIS

JARVIS, a system to connect LLMs with ML community

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LaneATT

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

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LIO-SAM

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

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models

Models and examples built with TensorFlow

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multinerf

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

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OpenGlue

Open Source Graph Neural Net Based Pipeline for Image Matching

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packnet-sfm

TRI-ML Monocular Depth Estimation Repository

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PVIO

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

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pyslam

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

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pytorch-superglue

SuperGlue: Learning Feature Matching with Graph Neural Networks

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pytorch-superpoint

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

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PyTorch-VAE

A Collection of Variational Autoencoders (VAE) in PyTorch.

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RobustVideoMatting

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

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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.

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SuperGluePretrainedNetwork

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

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vits

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

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