Neural Radiance Fields | NeRF
Neural Radiance Fields (NeRF) is a novel method for synthesizing 3D scenes using deep learning. It allows for generating high-quality, photo-realistic 3D models from 2D images or videos. NeRF represents a scene as a continuous 3D function that can be queried at any point in space to obtain the color and density of that point. This allows for rendering new views of the scene from any viewpoint and even allows for free-viewpoint navigation within the scene.
Implementing NeRF in Python using PyTorch
Below is a simple implementation of Neural Radiance Fields using PyTorch. This code demonstrates the basic idea behind NeRF and can be used as a starting point for more complex implementations.
import torch
import torch.nn as nn
import torch.nn.functional as F
class NeRF(nn.Module):
def __init__(self):
super(NeRF, self).__init__()
# Define the neural network architecture for representing the 3D scene
self.fc1 = nn.Linear(3, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, 3)
def forward(self, input_point):
# Pass the input_point through the neural network to obtain color and density
x = F.relu(self.fc1(input_point))
x = F.relu(self.fc2(x))
output_color_density = torch.sigmoid(self.fc3(x))
return output_color_density
# Sample usage of the NeRF model
input_point = torch.tensor([1.0, 2.0, 3.0])
nerf_model = NeRF()
output_color_density = nerf_model(input_point)
print(output_color_density)
This PyTorch code defines a simple NeRF model that takes a 3D input point and outputs the color and density of that point. The model consists of three fully connected layers and uses ReLU activation functions. The output is passed through a sigmoid function to obtain valid color and density values.
Conclusion
In this article, we have introduced the concept of Neural Radiance Fields (NeRF) and provided a simple implementation using PyTorch. NeRF has shown great potential in the field of computer graphics and 3D scene generation, and the provided code can be extended to create more sophisticated NeRF models. We hope this article has sparked your interest in NeRF and inspired you to explore this exciting area of research further.
Absolutely great video! Really helped clear up the papers seeing things implemented so straightforwardly. I have a few questions. What type of GPU did you use to train this model? When creating the encoding you initialize your out variable to have the position vector placed in it. (making the output [batch, ((3 * 2) * embedding_pos_dim) + 3] adding that trailing +3) Was there a reason for doing that? I mean adding it surely doesn't hurt. Batching the image creation is also a great idea for smaller gpus. Thanks again for such a great video!
I understand 10*6 for the pos enc. but why did you add 3 to it? Posencdim*6+3?
How would you add the coarse and fine networks improvement?
Hey I have a smaller question, Nerf takes 5d input, position and view direction, is there s way to get the view direction from a rotation matrix (3×3)?
Can you explain pytorch implementation for mip NeRF or zip NeRF? The github repos are very hard to understand
my appology i mean the argument "dataset" used in line 11 to 36 in the test function. Does it take the dataset from the llff format from the pkl file? i don get it, tks!!
Thank you for your fast reply! Your work is great! Im wondering about de “dataset” variable that you use in line 106. But where is defined? Could you clarify pls? I will buy your course as Im working on a Nerf thesis for my master in sc in ML. You mixed the transforms json file from colmap in a pkl file?
Does the code you've shared have the variable "dataset" defined? I don't see it. What is the output of the code a png file with rendered image? than is possible to get a mesh? thanks for your assistance
a practical question: how do people figure out the viewing angle and position for a scene that's been captured without that dome of cameras? the dome of cameras makes it easy to know the exact viewing angle and position, but what about just a dude with one camera walking around the scene taking photos of it from arbitrary positions? how do you get theta and phi in practice?
This is so nice. Just bought your course <3 But where can I get that pkl file? Can I just import lego demo files into that?
Just bought your course!! Pretty cool to find someone talking/teaching NeRFs, since LLMs and Diffusion models stormed and got all the attention haha
Does it generate the sample in 16 epochs?
Great video! can i get the dataset ?
can you share link for dataset
Awesome. Please keep doing in this field
Great video thank you very much!