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Computer Graphics Deep Learning NeRF Neural Radiance Fields (NeRF) Tutorials

Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 1

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Table of Contents

A few days back, while going through my photo gallery, I came across three pictures shown in Figure 1.

Figure 1: Three views of a staircase.

We can see that the pictures are of a staircase but from different viewpoints. I took three pictures because I wasn’t sure I could capture this beautiful scene with just one photo. I was worried I’d miss the right perspective.

This got me thinking, “what if there was a way to capture the entire 3D scene just from these pictures?”

That way, you (my audience) will be able to view exactly what I saw that day.

In NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, Mildenhall et al. (2020) proposed a method that turned out to be just what I needed.

Let’s look at what we have achieved by reproducing the paper’s method, as shown in Figure 2. For example, giving the algorithm some pictures of a plate of hotdogs from different viewpoints (top) could generate the entire 3D scenery (bottom) precisely.

Figure 2: Top: Various views of the scenery (input). Bottom: Generated 3D scenery (output).

Neural Radiance Fields (NeRF) bring together Deep Learning and Computer Graphics. While we at PyImageSearch have written a lot about Deep Learning, this will be the first time we talk about Computer Graphics. This series will be structured in a way that will best suit an absolute beginner. We expect no prior knowledge of Computer Graphics.

Note: A simpler implementation of NeRF won us the TensorFlow Community Spotlight Award.

Source: TensorFlow Community Spotlight Award

To learn about Computer Graphics and image rendering, just keep reading.

Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 1

Computer Graphics has been one of the wonders of modern technology. The applications of rendering realistic 3D scenes range from movies, space navigation, to medical science.

This lesson is part 1 of a 3-part series on Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras:

  • Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 1 (this tutorial)
  • Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 2 (next week’s tutorial)
  • Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 3

In this tutorial, we will cover the workings of a camera in the world of computer graphics. We will also introduce you to the dataset where we will work.

We have divided this tutorial into the following subsections:

  • World Coordinate Frame: representing the physical 3D world
  • Camera Coordinate Frame: representing the virtual 3D camera world
  • Coordinate Transformation: mapping from one coordinate frame to another
  • Projection Transformation: forming an image on a 2D plane (the camera sensor)
  • Dataset: understanding the dataset for NeRF

Imagine this. You are out with your camera and spot a beautiful flower. You think about the way you want to capture it. Now it is time to orient the camera, calibrate the settings, and click the picture.

This entire process of transforming the world scene into an image is encapsulated in a mathematical model commonly called the forward imaging model. We can visualize the model in Figure 3.

Figure 3: Forward Imaging Model.

The forward imaging model starts from a point in the world coordinate frame. We then transform this to the camera coordinate frame using coordinate transformation. After that, we use projection transformation to transform the camera coordinates onto the image plane.

World Coordinate Frame

All shapes and objects that we see in the real world exist in a 3D frame of reference. We call this frame of reference the world coordinate frame. Using this frame, we can locate any point or object in the 3D space quite easily.

Let’s take the point P in the 3D space as shown in Figure 4.

Figure 4: World Coordinate Frame.

Here, \hat{x}_{w}, \hat{y}_{w}, and \hat{z}_w represent the three axes in the world coordinate frame. The location of the point P is expressed through the vector X_{w} .

X_{w} = \begin{bmatrix} x_w \\ y_w \\ z_w \\ \end{bmatrix}

Camera Coordinate Frame

Like the world coordinate frame, we have another frame of reference called the camera coordinate frame, as shown in Figure 5.

Figure 5: Camera Coordinate Frame.

This frame sits at the center of a camera. Unlike the world coordinate frame, this is not a static frame of reference. We can move this frame as we are moving the camera around to take a picture.

The same point P from Figure 4 can now be located with both frames of reference, as shown in Figure 6.

Figure 6: World-to-camera coordinate transformation.

While in the world coordinate frame, the point is located by the X_{w} vector, in the camera coordinate frame, it is located by the X_{c} vector as shown in Figure 6.

X_{c} = \begin{bmatrix} x_c \\ y_c \\ z_c \\ \end{bmatrix}

Note: The location of point P does not change. Only the way of looking at the point changes with change in the frame of reference.

Coordinate Transformation

We have established two coordinate frames: the world and the camera. Now let us define a mapping between the two.

Let’s take the point P from Figure 6. Our goal is to build a bridge between the camera coordinates X_{c} and world coordinates X_{w}.

From Figure 5, we can say that

X_{c} = R \times (X_{w}-C_{w})

where

  • R represents the orientation of the camera coordinate frame with respect to the world coordinate frame. The orientation is represented by a matrix.

    R = \begin{bmatrix} r_{11} & r_{12} & r_{13} \\ r_{21} & r_{22} & r_{23} \\ r_{31} &r_{32} & r_{33} \\ \end{bmatrix}

    \begin{bmatrix} r_{11} & r_{12} & r_{13}\\ \end{bmatrix} → Direction of x_c in the world coordinate system.

    \begin{bmatrix} r_{21} & r_{22} & r_{23}\\ \end{bmatrix} → Direction of y_c in the world coordinate system.

    \begin{bmatrix} r_{31} & r_{32} & r_{33}\\ \end{bmatrix} → Direction of z_c in the world coordinate system.
  • C_w represents the position of the camera coordinate frame with respect to the world coordinate frame. The position is represented by a vector.

We can expand the above equation as follows

X_{c} = R\times (X_{w}-C_{w}) \\ \Rightarrow X_{c} = R \times X_w - R \times C_w \\ \Rightarrow X_{c} = R \times X_w + t

where t represents the translation matrix -(R \times C_w) . The mapping between the two coordinate systems has been devised but is not yet complete. In the above equation, we have a matrix multiplication along with a matrix addition. It is always preferable to compress things to a single matrix multiplication if we can. To do so we will use a concept called homogeneous coordinates.

The homogeneous coordinate system allows us to represent an N dimensional point x = [x_0, x_1, \dots, x_n] in an N+1 dimensional space \tilde{x} = [\tilde{x}_0, \tilde{x}_1, \dots, \tilde{x}_n, w] with a fictitious variable w \ne 0 such that

x_0 = \displaystyle\frac{\tilde{x}_0}{w}, \space x_1 = \displaystyle\frac{\tilde{x}_1}{w} ,\dots ,\space x_n = \displaystyle\frac{\tilde{x}_n}{w}

Using the homogeneous coordinate system we can transform X_w (3D) to \tilde{X}_w (4D).

X_w \equiv \begin{bmatrix} x\\ y\\ z\\ 1\\ \end{bmatrix} \equiv \begin{bmatrix} wx_w\\ wy_w\\ wz_w\\ w\\ \end{bmatrix} \equiv \begin{bmatrix} \tilde{x}\\ \tilde{y}\\ \tilde{z}\\ w\\ \end{bmatrix} \equiv \tilde{X}_w

With the homogeneous coordinates at hand, we can compress the equation … to just matrix multiplication.

\tilde{X}_c = \begin{bmatrix} x_c\\ y_c\\ z_c\\ 1\\ \end{bmatrix} = \begin{bmatrix} R & t \\ 0 & 1 \\ \end{bmatrix} \times \tilde{X}_w = \begin{bmatrix} r_{11} & r_{12} & r_{13} & t_x\\ r_{21} & r_{22} & r_{23} & t_y\\ r_{31} & r_{32} & r_{33} & t_z\\ 0 & 0 & 0 & 1 \end{bmatrix} \begin{bmatrix} x_w\\ y_w\\ z_w\\ 1\\ \end{bmatrix} \Rightarrow \boxed{ \tilde{X}_c = C_{ex} \times \tilde{X}_w}

where C_{ex} is the matrix that holds the orientation and position of the camera coordinate frame. We can call this matrix the Camera Extrinsic since it represents values like rotation and translation, both of which are external properties of the camera.

C_{ex} = \begin{bmatrix} r_{11} & r_{12} & r_{13} & t_x\\ r_{21} & r_{22} & r_{23} & t_y\\ r_{31} & r_{32} & r_{33} & t_z\\ 0 & 0 & 0 & 1 \end{bmatrix}

Projective Transformation

We started with a point P and its (homogeneous) world coordinates \tilde{X}_w. With the help of the camera extrinsic matrix C_{ex}, \tilde{X}_w was transformed into its (homogeneous) camera coordinates \tilde{X}_c.

Now we come to the final stage of actually materializing an image from the 3D camera coordinates \tilde{X}_c as shown in Figure 7.

Figure 7: Projecting a point on the image plane.

To understand projective transformation, the only thing we need is similar triangles. Let’s do a primer on similar triangles.

We have visualized similar triangles in Figures 8 and 9. With similar triangles

\displaystyle\frac{AB}{A' B'} = \displaystyle\frac{CB}{C' B'}

Figure 8: ABC and AB'C' are similar triangles.
Figure 9: ABC and AB'C' are similar triangles.

Yes, you guessed it correctly, AB'C' and ABP are similar triangles in Figure 10.

Figure 10: Can you spot the similar triangles?

From the properties of similar triangles, we can derive that

\displaystyle\frac{x_i}{f} = \displaystyle\frac{x_c}{z_c}; \space \displaystyle\frac{y_i}{f} = \displaystyle\frac{y_c}{z_c}

Therefore it follows:

x_i = f\displaystyle\frac{x_c}{z_c}\\ \space \\ y_i = f\displaystyle\frac{y_c}{z_c}\\

Now it is important to remember that the actual image plane is not a virtual plane but rather an array of image sensors. The 3D scene falls on this sensor which leads to the formation of the image. Thus x_i and y_i in the image plane can be substituted with pixel values u,v.

u = f\displaystyle\frac{x_c}{z_c} \\ \space \\ v = f\displaystyle\frac{y_c}{z_c} \\

A pixel in an image plane starts from the upper left-hand corner (0, 0), so it is also required to shift the pixels with respect to the center of the image plane.

u = f\displaystyle\frac{x_c}{z_c} + o_x \\ \space \\ v = f\displaystyle\frac{y_c}{z_c} + o_y

Here, o_x and o_y are the center points of the image plane.

Now we have a point from the 3D camera space represented in terms of u,v in the image plane. Again to make matrices agree, we have to express the pixel values using homogeneous representation.

Homogeneous representation of u,v, where u = \displaystyle\frac{\tilde{u}}{\tilde{w}} and v = \displaystyle\frac{\tilde{v}}{\tilde{w}}

\begin{bmatrix} u\\ v\\ 1\\ \end{bmatrix} \equiv \begin{bmatrix} \tilde{u}\\ \tilde{v}\\ \tilde{w}\\ \end{bmatrix} \equiv \begin{bmatrix} z_cu\\ z_cv\\ z_c\\ \end{bmatrix}

This can be further expressed as:

\begin{bmatrix} z_cu\\ z_cv\\ z_c\\ \end{bmatrix}= \begin{bmatrix} fx_c +z_co_x\\ fy_c +y_co_y\\ z_c\\ \end{bmatrix} = \begin{bmatrix} f & 0 & o_x & 0\\ 0 & f & o_y & 0\\ 0 & 0 & 1 & 0\\ \end{bmatrix} \begin{bmatrix} x_c\\ y_c\\ z_c\\ 1\\ \end{bmatrix}

Finally, we have:

\begin{bmatrix} \tilde{u}\\ \tilde{v}\\ \tilde{w}\\ \end{bmatrix} = \begin{bmatrix} f & 0 & o_x & 0\\ 0 & f & o_y & 0\\ 0 & 0 & 1 & 0\\ \end{bmatrix} \begin{bmatrix} x_c\\ y_c\\ z_c\\ 1\\ \end{bmatrix}

which can be expressed simply as

\tilde{u} = C_{in} \times \tilde{x}_c

where \tilde{x}_c is the set of vectors containing the location of the point in camera coordinate space and \hat{u} is the set of values containing the location of the point on the image plane. Respectively, C_{in} represents the set of values needed to map a point from the 3D camera space to the 2D space.

C_{in} = \begin{bmatrix} f & 0 & o_x & 0\\ 0 & f & o_y & 0\\ 0 & 0 & 1 & 0\\ \end{bmatrix}

We can call C_{in} the camera intrinsic since it represents values like focal length and center of the image plane along x and y axes, both of which are internal properties of the camera.

Dataset

Enough theory! Show me some code.

In this section, we will talk about the data with which we are going to work. The authors have open-sourced their dataset, and you can find it here. The link to the dataset was published in the official repository of NeRF. The dataset is structured as shown in Figure 11.

Figure 11: The drive folder containing the dataset.

There are two folders, nerf_synthetic and nerf_llff_data. Moving ahead, we will be using the synthetic dataset for this series.

Let’s see what is in the nerf_synthetic folder. The data in the nerf_synthetic folder is shown in Figure 12.

Figure 12: The synthetic data folder.

There are a lot of synthetic objects here. Let’s download one of them and see what is inside. We have chosen the “ship” dataset, but feel free to download any one of them.

After unzipping the dataset, you will find three folders containing images:

  • train
  • val
  • test

and three files containing the orientation and position of the camera.

  • transforms_train.json
  • transforms_val.json
  • transforms_test.json

To better understand the json files, we can open a blank Colab notebook and upload the transforms_train.json. We can now perform exploratory data analysis on it.

# import the necessary packages
import json
import numpy as np

# define the json training file
jsonTrainFile = "transforms_train.json"

# open the file and read the contents of the file
with open(jsonTrainFile, "r") as fp:
    jsonTrainData = json.load(fp)

# print the content of the json file
print(f"[INFO] Focal length train: {jsonTrainData['camera_angle_x']}")
print(f"[INFO] Number of frames train: {len(jsonTrainData['frames'])}")

# OUTPUT
# [INFO] Focal length train: 0.6911112070083618
# [INFO] Number of frames train: 100

We begin with importing the necessary packages json and numpy on Lines 2 and 3.

Then we the load json and read its values on Lines 6-10.

The json file has two parent keys called camera_angle_x and frames. We see that camera_angle_x corresponds to the camera’s field of view, and frames are a collection of metadata for each image (frame).

On Lines 13 and 14, we print the values of the json keys. Lines 17 and 18 show the output.

Let’s investigate frames a little further.

# grab the first frame
firstFrame = jsonTrainData["frames"][0]

# grab the transform matrix and file name
tMat = np.array(firstFrame["transform_matrix"])
fName = firstFrame["file_path"]

# print the data
print(tMat)
print(fName)

# OUTPUT
# array([[-0.92501402,  0.27488998, -0.26226836, -1.05723763],
#       [-0.37993318, -0.66926789,  0.63853836,  2.5740304 ],
#       [ 0.        ,  0.6903013 ,  0.72352195,  2.91661024],
#       [ 0.        ,  0.        ,  0.        ,  1.        ]])
# ./train/r_0

We grab the first frame on Line 20. Each frame is a dictionary containing two keys, transform_matrix and file_path, as shown on Lines 23 and 24. The file_path is the path to the image (frame) under consideration, and the transform_matrix is the camera-to-world matrix for that image.

On Lines 27 and 28, we print the transform_matrix and file_path. Lines 31-35 show the output.

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Summary

In this tutorial, we looked at some fundamental topics across Computer Graphics. This was essential to understand NeRF. While this is basic, it is still an important step to move forward.

We can recall what we have learned in three simple steps:

  1. The forward imaging model (taking a picture)
  2. World-to-Camera (3D to 3D) transformation
  3. Camera-to-Image (3D to 2D) transformation

At this point, we are also familiar with the dataset needed. This covers all of the prerequisites.

Next week we will look at the various underlying concepts of the paper: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. We will also learn how to implement these concepts using TensorFlow and Python.

We hope you enjoyed this tutorial, and be sure to download the dataset and give it a try.

Citation Information

Gosthipaty, A. R., and Raha, R. “Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 1,” PyImageSearch, 2021, https://pyimagesearch.com/2021/11/10/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-1/ 

@article{Gosthipaty_Raha_2021_pt1,
    author = {Aritra Roy Gosthipaty and Ritwik Raha},
    title = {Computer Graphics and Deep Learning with {NeRF} using {TensorFlow} and {Keras}: Part 1},
    journal = {PyImageSearch},
    year = {2021},
    note = {https://pyimagesearch.com/2021/11/10/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-1/},   
}

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About the Author

Hey there, fellow developer! Howdy doo!

We're Aritra Roy Gosthipaty and Ritwik Raha, two quirky Deep Learning (DL) engineers at PyImageSearch who just love teaching, academic research, and developing cool stuff in the fields of DL and Computer Vision. We're like detectives, always on the hunt for the most complex topics and novel ideas in research papers. And once we find them, we love breaking them down into easy-to-understand blogs that anyone could read, learn from, and implement on their own.

If you like what you see and want to connect with us, feel free to drop Aritra a line on Twitter at @ariG23498, or Ritwik at @ritwik_raha. We promise we are not bots....well, not completely!

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