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https://github.com/shub-garg/nerf-pytorch-implementation
https://github.com/shub-garg/nerf-pytorch-implementation
Last synced: 3 days ago
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- Host: GitHub
- URL: https://github.com/shub-garg/nerf-pytorch-implementation
- Owner: shub-garg
- License: mit
- Created: 2024-06-20T22:02:29.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-07-20T10:11:51.000Z (7 months ago)
- Last Synced: 2024-07-20T11:26:17.731Z (7 months ago)
- Language: Python
- Size: 47.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NERF-torch
## Overview
This repository contains a PyTorch implementation of **NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis**. NeRF is a groundbreaking method introduced by Mildenhall et al. in their ECCV 2020 paper, which enables the synthesis of novel views of complex 3D scenes.
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## Description
NeRF represents scenes as neural radiance fields, enabling high-fidelity 3D scene reconstruction and view synthesis. This implementation leverages PyTorch for efficient computation and training. Whether you're a researcher looking to dive into state-of-the-art neural rendering or a developer interested in 3D graphics and computer vision, this project provides a robust starting point.
## Getting Started
Follow these steps to set up your environment, install dependencies, and begin training your own NeRF models.
### Setup Environment
To configure the Python virtual environment and install dependencies, run the following commands in your shell:
```sh
# Clone the repository
git clone https://github.com/shub-garg/NERF-torch.git
cd NERF-torch
```
# Create virtual environment
```
virtualenv venv -p python3.8
source venv/bin/activate
```
# Install dependencies
```
pip install -r requirements.txt
```
You may use alternative tools like conda if preferred. Ensure the correct dependency versions are installed to reproduce the results.
# Download Dataset
Download the synthetic dataset required for training by running the script below:
```bash
sh scripts/download_data.sh
```
This script creates a data directory under the project root (torch-NeRF) and downloads datasets provided by the original NeRF authors.
# Training
The configuration is pre-set for the lego scene from the Blender dataset. Start training by running:
```bash
python torch_nerf/runners/run_train.py
```
# Rendering
Once the scene representation is trained, render it using:
```bash
python torch_nerf/runners/run_render.py
```
Update the yaml configuration file under config/train_params to specify the path to the checkpoint file. The rendered images will be saved in the render_out directory.
# Create Video
To compile the rendered frames into a video, use the following script:
```bash
python scripts/make_video.py
```
# Images
