Introduction
In recent years, neural rendering models have emerged as a revolutionary technology for instant 3D rendering of high-resolution 3D scenes in seconds. One of the most notable models is Neural Radiance Fields (NeRF). In this blog post, we will discuss the basics of NeRF and its impact on different fields, such as graphics, architecture, and entertainment. Moreover, we will take a look at some of the available implementations of NeRF, including PyTorch implementation, Luma AI and NVIDIA’s Instant NeRF..
What is NeRF?
NeRF is a neural network that uses a collection of 2D images taken at different angles to render realistic 3D scenes. The neural network requires several images taken from different positions around the scene, as well as the camera position of each of those shots. It uses the input to fill in the blanks and trains a small neural network to reconstruct the scene by predicting the color of light radiating in any direction, from any point in 3D space. The output is a photorealistic 3D model that can be used in various applications.
PyTorch implementation of NeRF
There is a PyTorch implementation of NeRF that reproduces the results while running 1.3 times faster than the original. The PyTorch implementation is available on GitHub and includes all the necessary code to train and test NeRF models. The implementation is highly modular, allowing researchers to experiment with different network architectures, loss functions, and optimization methods. Moreover, the PyTorch implementation supports distributed training on multiple GPUs, making it possible to train large-scale NeRF models. There is a great howto on how to set up and run PyTorch on Google Colab.
credits: https://www.matthewtancik.com/nerf
Luma AI
Luma AI is a company that has made significant contributions to the development and implementation of NeRF technology. They have developed a groundbreaking NeRF-based app for 3D scanning that is changing the game in the world of 3D modeling and visualization.
Their app, called LumaForge, allows users to capture 3D scans of real-world objects and environments using just their smartphone camera. The app utilizes NeRF technology to generate highly realistic 3D models from these scans, with an unprecedented level of detail and accuracy.
One of the most impressive aspects of the LumaForge app is its ability to handle complex and dynamic scenes. Traditional 3D scanning methods often struggle with capturing scenes that include moving objects or changing lighting conditions, but NeRF-based methods are able to handle these challenges with ease. This makes the LumaForge app a powerful tool for a wide range of applications, from architecture and engineering to film and video production.
Luma AI has also been involved in the development of other NeRF-based applications, including the synthesis of novel views of complex scenes using an underlying continuous volumetric scene function. Their research in this area has demonstrated the potential for NeRF technology to revolutionize the way we create and interact with 3D models and visualizations.
Overall, Luma AI is an exciting company to watch in the world of 3D modeling and visualization. Their contributions to the development of NeRF technology are making it easier and more accessible than ever before to create highly realistic 3D models of real-world objects and environments.
NVIDIA: instant Nerf
Traditional methods of creating 3D scenes take hours or longer, depending on the complexity and resolution of the visualization. However, Instant NeRF cuts rendering time by several orders of magnitude. It uses a technique developed by NVIDIA called multi-resolution hash grid encoding, optimized to run efficiently on NVIDIA GPUs. Using a new input encoding method, researchers can achieve high-quality results using a tiny neural network that runs rapidly.
The model was developed using the NVIDIA CUDA Toolkit and the Tiny CUDA Neural Networks library. As it is a lightweight neural network, it can be trained and run on a single NVIDIA GPU, running fastest on cards with NVIDIA Tensor Cores. This technique can be used to train robots and self-driving cars to understand the size and shape of real-world objects by capturing 2D images or video footage of them.
Applications of Instant NeRF:
Instant NeRF has a wide range of applications in various fields, including:
- Virtual Reality: Instant NeRF could be used to create avatars or scenes for virtual worlds.
- Video Conferencing: It can capture video conference participants and their environments in 3D.
- 3D Digital Maps: It can reconstruct scenes for 3D digital maps.
- Architecture and Entertainment: It can rapidly generate digital representations of real environments that creators can modify and build on.
Moreover, Instant NeRF is one of the first models of its kind to combine ultra-fast neural network training and rapid rendering, making it almost instant. It is comparable to the groundbreaking effect of digital cameras and JPEG compression on 2D photography, as it vastly increases the speed, ease, and reach of 3D capture and sharing.
Accelerating Multiple AI Challenges:
NVIDIA researchers are exploring how this input encoding technique might be used to accelerate multiple AI challenges, including reinforcement learning, language translation, and general-purpose deep learning algorithms. As neural networks become more common in computer vision, natural language processing, and other AI applications, methods to accelerate their training and inference are becoming increasingly important. NVIDIA’s Instant NeRF and similar techniques may play a significant role in achieving those gains.
