How to Set Up NVIDIA Instant NeRF: From Images to a 3D Scene

What will you learn in this guide?

NVIDIA Instant NeRF learns a scene representation from images, letting you view the scene from different camera positions. The workflow involves building the software, preparing photos or video, calculating camera positions, and training the scene. You can also create a camera path in the interface and render an animation.

The author of NVIDIA’s technical guide says it’s possible to complete an initial NeRF experiment—from building the codebase to preparing images and training the scene—in as little as an hour. This is not a performance guarantee for every computer; it describes the duration of the hands-on experience in the guide. The training stage itself can take a few minutes.

Requirements and preparation

The guide lists Visual Studio 2019, the latest version of CUDA Toolkit, Python 3.9, and CMake 3.22 as requirements for building the codebase. During the CMake build, make sure it’s using Python 3.9. Since the source doesn’t specify a particular CUDA Toolkit version number, check the latest build instructions in the code repository before installing.

If you’re new to building software, you can follow the detailed instructions in the community fork referenced by the guide. According to the source, building the codebase is a more straightforward process for people with programming and data science experience. Since command-line instructions aren’t provided here, follow the official build instructions rather than guessing and adding commands that aren’t in the repository.

You can use photos or video as scene input. Images should contain sharp, overlapping frames to help COLMAP determine camera positions. In other words, capture the same scene from different angles throughout the shoot; don’t start with a set of blurry or disconnected images.

Instant NeRF workflow, step by step

1. Prepare the build environment. Install Visual Studio 2019, CUDA Toolkit, Python 3.9, and CMake 3.22. Check that CMake is using Python 3.9, then build the codebase according to the project instructions. NVIDIA’s guide also points beginners who run into setup issues to the annotated steps in the community fork.

2. Capture the scene in photos or video. Photograph the same space or object from different positions. Keep details sharp in the frames and make sure each image has visual features in common with the next. The basic approach recommended in the source is to prepare a set of images that follows photogrammetry principles.

3. Determine camera positions. In the first stage of the Instant NeRF workflow, COLMAP calculates camera positions from the images. Before training, review your image set: the images should show shared parts of the scene and be sufficiently sharp. Once the camera positions are ready, you can train the initial NeRF scene.

4. Launch the Testbed interface. Open the compiled Testbed.exe file through Anaconda. According to NVIDIA’s guide, training starts automatically when the interface opens. During training, monitor the loss graph and the scene view. The guide says much of the improvement in image quality can be seen in the first 30 seconds, though the result may continue to improve for a few more minutes.

5. Stop training when appropriate. A flattening loss graph can be a sign that it’s time to consider stopping training. According to the guide, stopping training can improve the viewer’s frame rate. So don’t focus only on training for longer; assess scene quality and viewing responsiveness together.

6. Create a camera animation. Move to a camera position in the scene and add a keyframe using Add from Cam. The camera path is created with Bézier curves. Use Read to preview it. If the motion looks right, save the camera path and use the render script in the codebase’s scripts folder to produce a full-quality video.

In architecture, interiors, and visualization workflows

The aspect of Instant NeRF covered in this guide is its ability to create a scene from images and let you examine it from different camera positions. NVIDIA’s guide emphasizes that the method can include the background as part of the scene. This approach can be explored in experimental work by architects, interior designers, and visualization teams who want to consider a space together with its surroundings, rather than as a single object.

However, the source doesn’t provide information about compatibility with specific CAD or BIM software, production delivery formats, or direct integration into project workflows. It mentions a CUDA Toolkit requirement, but doesn’t list a specific graphics card model or minimum system specifications. Offices should therefore check whether their current workstations are compatible and review the latest build instructions in advance. It’s more accurate to treat Instant NeRF as a way to explore image-based scene creation, rather than assume it replaces existing modeling and rendering tools.

Common mistakes

• Using blurry images or images with too little overlap: COLMAP determines camera positions from the images. Before training, make sure your image set contains sharp, connected frames from different angles.

• Overlooking the Python version during the build: The guide specifies Python 3.9 and CMake 3.22. Check that CMake uses the correct Python version during the build.

• Judging training by duration alone: Monitor the loss graph and viewer performance as well as image quality. It may be appropriate to stop training when the graph flattens.

• Rendering before previewing the camera path: First review the motion with Read, then save the camera path and run the render script.

Next steps

Starting with a small scene that’s easy to reshoot makes it easier to observe how image quality affects the result. Try photos and video as separate inputs and compare the results under your own shooting conditions. You can also explore the camera controls and debugging views in the interface; the guide says these options are available but doesn’t explain each one in detail.

If you’re aiming to create an animation, build the camera path first and preview it with Read. As a final step, save the camera path and use the render script to create a full-quality video. If you run into setup or build problems, recheck the current project instructions alongside the components specified in the guide.

Source and license

This guide is adapted into Turkish based on the setup, image preparation, training, and animation information in NVIDIA’s technical guide, “Getting Started with NVIDIA Instant NeRFs.” The source states that license information is provided freely.