Network

Band-limited Coordinate Networks for Multiscale Scene Representation

Project Page | Video | Paper Official PyTorch implementation of BACON.BACON: Band-limited Coordinate Networks for Multiscale Scene RepresentationDavid B. Lindell*,Dave Van Veen,Jeong Joon Park,Gordon WetzsteinStanford University Quickstart To setup a conda environment use these commands conda env create -f environment.yml conda activate bacon # download all datasets python download_datasets.py Now you can train networks to fit a 1D function, images, signed distance fields, or neural radiance fields with the following commands.

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Real Time

FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

This repository contains the code (in PyTorch) for the “FADNet++” paper. Contents Introduction Usage Results Acknowledgement Contacts Introduction We propose an efficient and accurate deep network for disparity estimation named FADNet with three main features: It exploits efficient 2D based correlation layers with stacked blocks to preserve fast computation. It combines the residual structures to make the deeper model easier to learn. It contains multi-scale predictions so as to exploit a multi-scale weight scheduling training technique to improve the accuracy. […]

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Video

Deepstream python play rtsp h264

deepstream python play rtsp h264 n gstreamer python play rtsp h264 and h265 n common=> deepstream_python_apps=>apps n dstest3_pgie_config.txt ： test pgie n deepstream_rtsp_h264.py ：play one rtsp h264 n deepstream_rtsps_h264.py ：play Multiple rtsp h264 n deepstream_videos_h264.py ：play Multiple video h264 n gstreamer_test_h264.py ：play one rtsp h264 n gstreamer_test_h265.py ：play one rtsp h265 n GitHub View Github    

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COVID-19

Backend code for covid-polygraph

Covid-polygraph, a set of Machine Learning-driven fact-checking tools that aim to address the issue of misleading information related to COVID-19. Project is extended based on our CS3244 Team Projectand more on code reference and reuse can be found in the Offline Training Pipeline Repository. How to run the backend code To run the code, use Docker: docker build . -t covid-polygraph:latest Afterwards, you can start the backend locally using the docker image built. For our team, we deploy this image […]

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Wrapper

A simple wrapper to analyse and visualise reinforcement learning agents’ behaviour in the environment

Visrl (pronounced “visceral”) is a simple wrapper to analyse and visualise reinforcement learning agents’ behaviour in the environment. Reinforcement learning requires a lot of overhead code to inspect an agent’s behaviour visually, typically through env.render(). Visrl allows users to easily intervene and switch between agent control and human control, and allows inserting a breakpoint in the game state to pause only at a relevant state of interest. Features Set action hotkeys Human intervention: Take actions 1 step at a time […]

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Telegram

Telegram bot to stream videos in telegram voicechat for both groups and channels. Supports live steams, YouTube videos and telegram media

Telegram bot to stream videos in telegram voicechat for both groups and channels. Supports live steams, YouTube videos and telegram media. Variables See Variables Pre Requisites Recommended Optional Vars DATABASE_URI: MongoDB database Url, get from mongodb. This is an optional var, but it is recomonded to use this to experiance the full features. HEROKU_API_KEY: Your heroku api key. Get one from here HEROKU_APP_NAME: Your heroku apps name. FILTERS: Filter the search for channel play. Channel play means you can play […]

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Generator

The code needed to train Mega-NeRF models and generate the sparse voxel octrees

This repository contains the code needed to train Mega-NeRF models and generate the sparse voxel octrees used by the Mega-NeRF-Dynamic viewer. The codebase for the Mega-NeRF-Dynamic viewer can be found here. Note: This is a preliminary release and there may still be outstanding bugs. Citation @misc{turki2021meganerf, title={Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs}, author={Haithem Turki and Deva Ramanan and Mahadev Satyanarayanan}, year={2021}, eprint={2112.10703}, archivePrefix={arXiv}, primaryClass={cs.CV} } Demo Setup    

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