Torch-Ngp

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torch-ngp is an open-source research tool that provides a PyTorch CUDA extension implementation of instant-ngp for SDF and NeRF, complete with a GUI.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is torch-ngp?

torch-ngp offers a PyTorch CUDA extension implementation of instant-ngp, supporting both Signed Distance Functions (SDF) and Neural Radiance Fields (NeRF). It includes a graphical user interface (GUI) for training and visualization. The repository also features PyTorch implementations of TensoRF, adapted to instant-ngp's NeRF framework, and CCNeRF for compressible-composable NeRF via rank-residual decomposition. Additionally, it provides an implementation of D-NeRF for dynamic scenes and experimental features like text-guided NeRF editing. The tool supports various datasets and offers options for different backbones and optimization techniques, making it a versatile platform for neural graphics research.

Best used for

Ideal for researchers and developers who need to implement and experiment with neural graphics primitives like NeRF and SDF. Especially valuable for those looking to leverage PyTorch for instant-ngp, TensoRF, CCNeRF, and D-NeRF, with the added benefit of a GUI for interactive training and visualization.

Common actions

implement neural graphics

train NeRF models

visualize 3D scenes

experiment with SDF

develop dynamic scenes

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Capabilities

Key features

PyTorch CUDA extension
Instant-ngp implementation
SDF and NeRF support
GUI for training/visualization
TensoRF integration
CCNeRF implementation
D-NeRF for dynamic scenes

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the hardware requirements for running torch-ngp?

torch-ngp requires a GPU with CUDA architecture >= 70 for optimal performance with the --ff flag. For GPUs with lower architecture, --tcnn can still be used, though speed may be slower. It has been tested on Ubuntu with various CUDA and PyTorch versions.

How do I handle 'CUDA Out Of Memory' errors?

If you encounter CUDA Out Of Memory errors, try turning off the --preload flag (if using -O, switch to --fp16 --cuda_ray). Another solution is to manually set the downscale parameter in NeRFDataset to reduce image resolution, thus lowering memory usage.

Can I use my own custom datasets with torch-ngp?

Yes, torch-ngp supports custom datasets. You can preprocess your own videos or image collections using the provided scripts like colmap2nerf.py to convert them into the nerf-compatible format, allowing you to train and visualize your unique scenes.

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Content & Design › 3D & Animation

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