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nvlabs/instant-ngp

17,392CudaAudience · researcherComplexity · 5/5Setup · hard

TLDR

NVIDIA research tool that turns a set of photos or 3D data into an interactive scene you can fly through, training a neural radiance field (NeRF) in under 5 seconds using a GPU-accelerated multiresolution hash encoding.

Mindmap

mindmap
  root((instant-ngp))
    What it does
      Fast NeRF training
      Interactive 3D scenes
      4 neural graphic primitives
    Neural Primitives
      NeRF from photos
      Signed distance functions
      Neural images
      Neural volumes
    Tech Stack
      NVIDIA CUDA
      tiny-cuda-nn library
      Python bindings
    Use Cases
      Scene capture from video
      3D mesh export
      NeRF research prototyping
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Things people build with this

USE CASE 1

Capture a real-world scene from phone-camera video and explore it interactively as a NeRF in 3D.

USE CASE 2

Export a trained NeRF or signed distance function to a polygon mesh for import into 3D software like Blender.

USE CASE 3

Prototype neural radiance field or neural image compression experiments using the live GUI or Python bindings.

Tech stack

CUDAC++Pythontiny-cuda-nnOpenGL

Getting it running

Difficulty · hard Time to first run · 1h+

CUDA-only, requires an NVIDIA GPU, pre-built downloads only for Windows, Linux users must build from source with CUDA toolkit installed.

License not specified in the explanation.

In plain English

Instant Neural Graphics Primitives, or instant-ngp, is a research project from NVIDIA that turns photos or 3D data into interactive visual scenes you can fly around, and does it very quickly. The headline trick is training a NeRF, which stands for neural radiance field, of a fox from a small set of photos in under 5 seconds rather than the hours it traditionally took. Beyond NeRFs, the same code handles three other neural graphics primitives: signed distance functions (a way of describing 3D shapes), neural images (storing a single image inside a small neural network), and neural volumes for things like clouds and smoke. The way it works is that all four primitives are represented by a small neural network, a multilayer perceptron, combined with a multiresolution hash input encoding, the trick described in the team's SIGGRAPH 2022 paper. The network and encoding run on top of the project's tiny-cuda-nn library, which is what makes training fast enough to feel interactive. The application ships as a GUI where you drag a dataset or 3D file into the window and watch it train and render live, it also offers a camera path editor for making videos, snapshot save and load, a VR mode, and conversions from NeRF or SDF to a regular polygon mesh. You would use this to capture a real-world scene from a phone-camera video and explore it in 3D, prototype a NeRF-based effect, or experiment with these compression-style neural representations. Pre-built downloads are provided for Windows, Linux users and anyone wanting the Python bindings build from source. The code is written in CUDA for NVIDIA GPUs. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
Walk me through capturing a 360-degree video of an object with my phone and converting it into a NeRF using instant-ngp on Windows.
Prompt 2
How do I export a trained instant-ngp NeRF scene to a polygon mesh I can import into Blender?
Prompt 3
Show me how to use the instant-ngp Python bindings to train a NeRF on a custom dataset and render frames programmatically.
Prompt 4
How do I use instant-ngp's neural SDF mode to reconstruct a 3D shape from a point cloud or 3D mesh file?
Prompt 5
What is the multiresolution hash input encoding in instant-ngp and why does it make NeRF training so much faster than before?
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