F2-Nerf

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f2-nerf is an open-source 3D & Animation tool that enables fast neural radiance field training. It supports free camera trajectories for efficient 3D scene reconstruction and novel view synthesis.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is f2-nerf?

f2-nerf is an open-source project designed for fast neural radiance field (NeRF) training, specifically optimized for scenarios involving free camera trajectories. Built primarily on LibTorch, this tool provides a robust framework for efficient 3D scene reconstruction and novel view synthesis. Users can train F2-NeRF on custom data, including images processed with COLMAP or hloc, and generate camera poses. It also includes scripts for rendering test images and creating render paths by interpolating input camera poses. The project leverages several powerful libraries such as tiny-cuda-nn for fast MLP training, happly for PLY I/O, and eigen for linear algebra, making it a comprehensive solution for advanced NeRF applications.

Best used for

Ideal for researchers and developers who need to train neural radiance fields quickly, reconstruct 3D scenes from various camera angles, and synthesize novel views. Especially valuable for projects requiring efficient processing of free camera trajectories and custom dataset integration.

Common actions

train neural radiance fields

reconstruct 3D scenes

synthesize novel views

process camera trajectories

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Capabilities

Key features

Fast NeRF training
Free camera trajectories
Custom data support
COLMAP/hloc integration
Novel view synthesis
Render path generation

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the primary dependencies for f2-nerf?

The development of f2-nerf is primarily based on LibTorch. It also utilizes other libraries such as happly for PLY files, stb_image for image files, tiny-cuda-nn for fast MLP training, eigen for linear algebra, yaml-cpp for YAML files, hydra for configuration, and cnpy for npy files.

Can I train f2-nerf with my own datasets?

Yes, f2-nerf supports training on custom data. You need to ensure your images are correctly placed and then run COLMAP SfM or hloc to generate camera poses. After that, you can use the provided scripts to convert the COLMAP output and train F2-NeRF.

How can I generate a render path for novel views?

You can generate a render path by interpolating input camera poses using the provided script `scripts/inter_poses.py`. This script allows you to specify key poses and the number of output poses, creating a `poses_render.npy` file that can then be used to render novel images.

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