NeRF (Neural Radiance Fields) is a cutting-edge approach for rendering 3D scenes from 2D images, and with the Taichi and PyTorch implementation, you can leverage this technology to create stunning visualizations. In this guide, we will walk you through the installation process, training procedures, and potential troubleshooting steps. Let’s dive in!

Installation Guide

Before you can start training your NeRF models, you need to set up your environment. Follow these steps to get everything ready:

1. Install PyTorch using the command below. Ensure to update the URL with your installed CUDA Toolkit version number:

python -m pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116

2. Install the nightly version of Taichi:

pip install -U pip

pip install -i https://pypi.taichi.graphics/simple taichi-nightly

3. Install the required packages:

pip install -r requirements.txt

4. If you plan to train with your own videos, install COLMAP via:

sudo apt install colmap

For more installation details, visit COLMAP installation guide.

Training with Preprocessed Datasets

Synthetic NeRF

Begin by downloading the Synthetic NeRF dataset and unzip it, ensuring the folder name remains unchanged. To train the Lego scene from scratch, run the following command:

bash scripts/train_nsvf_lego.sh

Performance Metrics

Scene	Average PSNR	Training Time (20 epochs)
Lego	35.0	208s

To reach optimal performance, adhere to these steps:

• Ensure your workstation runs on Linux and is equipped with an RTX 3090 Graphics card.
• Follow the installation steps outlined above.
• Uncomment --half2_opt for enhanced performance, specifically on Linux with Pascal architecture.

Using 360_v2 Dataset

Download the 360 v2 dataset, unzip it, and keep the folder name unchanged. You can then train your model using:

bash scripts/train_360_v2_garden.sh

Training with Your Own Video

If you prefer to train with your video, place it in the data folder and use this command:

bash scripts/train_from_video.sh -v your_video_name -s scale -f video_fps

Adjust scale and video_fps as required to optimize your training session.

Mobile Deployment

You can deploy your NeRF rendering pipeline on mobile devices easily using Taichi AOT! Performance benchmarks show that:

• iPad Pro (M1): 22.4 fps
• iPhone 14 Pro Max: 18 fps
• iPhone 14: 13.5 fps

Frequently Asked Questions (FAQ)

Here’s some common troubleshooting information to assist you:

• Q: Is CUDA the only supported Taichi backend?
  A: While CUDA is recommended for optimal performance during training, switching to Taichi Vulkan backend is possible.
• Q: I’m seeing an OOM (Out of Memory) error on my GPU. What should I do?
  A: Reduce the batch_size passed to train.py. For RTX 3060Ti, a batch_size of 2048 is advisable.

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