Instant-Ngp

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instant-ngp is an open-source tool for training neural graphics primitives, offering lightning-fast NeRF and more. It enables rapid training and rendering of 3D scenes from 2D images.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is instant-ngp?

instant-ngp is an open-source implementation of four neural graphics primitives: neural radiance fields (NeRF), signed distance functions (SDFs), neural images, and neural volumes. It allows users to train and render MLPs with multiresolution hash input encoding using the tiny-cuda-nn framework. The tool features an interactive GUI with comprehensive controls, including a VR mode, snapshot saving/loading, a camera path editor for videos, NeRF->Mesh and SDF->Mesh conversion, and camera pose/lens optimization. It supports various NeRF-compatible datasets and provides options for both Windows and Linux users, with Python bindings available for automated experiments.

Best used for

Ideal for researchers and developers who need to rapidly train NeRF models, render complex 3D scenes from images, and explore neural graphics primitives. Especially valuable for those working with 3D reconstruction, computer vision, and virtual reality applications requiring high-performance neural rendering.

Common actions

train neural graphics

render 3D scenes

create NeRF models

convert NeRF to mesh

explore neural primitives

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Capabilities

Key features

Neural Radiance Fields (NeRF)
Signed Distance Functions (SDF)
Neural Images
Neural Volumes
Interactive GUI
VR mode
Camera path editor

Target Audience

researcherdeveloperengineer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for instant-ngp?

instant-ngp requires an NVIDIA GPU (tensor cores recommended), a C++14 capable compiler (Visual Studio 2019/2022 for Windows, GCC/G++ 8+ for Linux), and CUDA 10.2 or higher. CMake v3.21+ is also necessary. Optional requirements include Python 3.8+ for bindings and OptiX 7.6+ for faster mesh SDF training.

Can instant-ngp use multiple GPUs?

instant-ngp primarily uses a single GPU for training and rendering. However, it can utilize multiple GPUs for VR rendering, dedicating one GPU per eye. Users can select a specific GPU using the CUDA_VISIBLE_DEVICES environment variable for single-GPU operations.

How can I save and load trained models in instant-ngp?

You can save and load trained models, referred to as "snapshots," directly through the interactive GUI's "Snapshot" section. Alternatively, for programmatic control, the Python bindings provide `load_snapshot` and `save_snapshot` functions, as demonstrated in the `scripts/run.py` example.

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