Taichi-Nerfs

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taichi-nerfs is a Content & Design tool that provides a PyTorch + Taichi implementation of an instant-ngp NeRF training pipeline. It enables real-time neural radiance field rendering and mobile deployment.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is taichi-nerfs?

taichi-nerfs offers a robust PyTorch and Taichi implementation for instant-ngp NeRF training pipelines, designed for researchers and developers working with neural radiance fields. This open-source tool allows users to train NeRF models from preprocessed datasets like Synthetic NeRF and 360_v2, or directly from their own videos. A key differentiator is its ability to deploy NeRF rendering pipelines on mobile devices using Taichi AOT, achieving real-time interactive performance on iOS devices such as iPad Pro and iPhone 14 Pro Max. It also serves as a backend for text-to-3D projects like stable-dreamfusion, expanding its utility in 3D content creation. The project emphasizes performance, with optimizations like half2 optimization for compatible GPUs.

Best used for

Ideal for researchers and developers who need to implement and train various NeRF models, deploy real-time 3D rendering on mobile devices, and preprocess custom videos for NeRF training. Especially valuable for those integrating NeRF technology into text-to-3D projects or requiring high-performance, GPU-accelerated solutions.

Common actions

implement NeRF variants

train neural radiance fields

deploy 3D models mobile

preprocess video for NeRF

integrate with PyTorch

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Capabilities

Key features

NeRF training pipeline
Mobile deployment (iOS)
Real-time rendering
Video-to-NeRF training
PyTorch + Taichi integration
Text-to-3D backend

Target Audience

professor

Integrations

pytorch

Pricing & Plans

Open Source

Free

FAQs

What Taichi backend is primarily supported for training?

For the most efficient interop with PyTorch, training is primarily tested with the Taichi CUDA backend. While a Vulkan backend is technically possible, the CUDA backend is recommended for optimal performance and compatibility with the PyTorch ecosystem.

How can I resolve Out of Memory (OOM) errors on my GPU?

If you encounter an OOM error, you should reduce the `batch_size` parameter passed to `train.py`. The default `batch_size=8192` is suitable for an RTX3090, but smaller GPUs like an RTX3060Ti may require a `batch_size=2048` or lower.

Can I train NeRFs using my own video footage?

Yes, you can train NeRFs from your own videos. Simply place your video in the `data` folder and use the provided script, specifying parameters like video path, scale, and video FPS to generate sufficient images for training.

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Research & Education › Academic Research Coding & Development › Open Source & Models Research & Education › Scientific Computing

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