NERFs (No, not that kind) - Computerphile

Summary

This video explores Neural Radiance Fields (NeRF), a cutting-edge AI technique for generating 3D reconstructions from 2D images. The host, with the help of a PhD student, explains how NeRF uses RGB images to create detailed 3D scenes, contrasting it with traditional methods like point clouds and meshes. They discuss the process of training a neural network to 'see' and render 3D objects, emphasizing the importance of multiple viewpoints for accurate reconstruction. The video also touches on the limitations, such as the need for a large dataset and the challenges of capturing dynamic scenes, while highlighting NeRF's potential in revolutionizing 3D rendering and asset creation.

Takeaways

• 🌐 The script introduces Neural Radiance Fields (NeRF), a technology for generating new views of scenes from a series of RGB images.
• 🎓 Lewis, a PhD student, explains NeRF, which he works with as part of his PhD, to the audience.
• 🌳 NeRF reconstructs 3D scenes with high detail from simple RGB images, unlike traditional 3D reconstruction methods like point clouds or meshes.
• 🖼️ Rendering with NeRF involves shooting rays into the environment and querying a neural network for color and density at various points along the ray.
• 🔍 NeRF's 3D representation allows it to understand the environment, unlike other generative models like diffusion which lack a 3D context.
• 📸 For effective training, NeRF requires a substantial number of images, ideally 250 or more, to achieve a good reconstruction.
• 📹 The script discusses the challenges of capturing a dynamic scene like a Christmas tree with NeRF, including issues like motion blur and overfitting.
• 🚫 Changes in the scene or photobombing can lead to artifacts like 'floaters' where the model struggles to place certain pixels accurately.
• 📈 Despite its limitations, NeRF can be used to quickly generate 3D representations from images, which can then be converted into meshes for use in games or other applications.
• 🔮 The future of 3D rendering may involve technologies like NeRF, but there are also emerging rivals like GAN splatting that show promise.

Q & A

What are Neural Radiance Fields (NeRF)?

Neural Radiance Fields, or NeRF, is a method used in AI literature for generating new views of scenes from a series of RGB images. It reconstructs 3D scenes using a neural network, unlike traditional 3D reconstruction methods such as point clouds, voxel grids, or meshes.

How does NeRF differ from traditional 3D reconstruction methods?

Traditional 3D reconstruction methods like point clouds, voxel grids, and meshes are discrete and can become complicated with many points or faces. NeRF, on the other hand, reconstructs scenes in high-quality detail using only RGB images, encoding the 3D scene within the parameters and weights of a neural network.

How does the rendering process in NeRF work?

In NeRF, rendering involves shooting rays into the environment and querying a series of points along these rays. The neural network then provides color and density for each point, which is different from traditional rendering methods that might use rasterization or ray tracing.

What is the significance of density in the context of NeRF?

Density in NeRF indicates whether a point along a ray is within an object or in empty space. A density of zero corresponds to empty space, while a density of one or more signifies that the point is inside an object, which is crucial for 3D representation.

Why is capturing multiple images from different angles important for NeRF?

Capturing multiple images from various angles is essential for NeRF to properly reconstruct a 3D scene. It allows the neural network to understand the environment from different perspectives, which is necessary for accurate rendering when the camera viewpoint changes.

What are the downsides of using NeRF for 3D reconstruction?

One downside of NeRF is the need for a large number of images for good reconstruction quality. Additionally, the method can struggle with scenes where objects change position or are not fully captured, leading to artifacts or noise in the rendered image.

How many images are typically needed for a good NeRF reconstruction?

For a good reconstruction, at least 250 images are recommended. However, the quality can vary depending on the technique used and the complexity of the scene.

What is the role of the camera positions in NeRF?

Camera positions are crucial in NeRF as they provide the spatial context for the images captured. They help the neural network understand where each image was taken from, which is necessary for reconstructing the 3D scene accurately.

Can NeRF be used to create 3D assets for games or other applications?

Yes, NeRF can be used to extract the 3D volume of objects, which can then be converted into meshes. This can speed up the creation of 3D assets for use in games or other applications, as it requires fewer manual design and modeling steps.

What is the current state of NeRF in terms of real-time rendering?

NeRF is not suitable for real-time rendering due to its computational intensity. It can take several seconds to render a single image, making it more suitable for pre-rendered scenes rather than real-time applications.

Are there any alternatives to NeRF for 3D reconstruction?

Yes, there are alternatives such as GAN splatting, which is a newer method also providing impressive results for 3D reconstruction and is worth exploring for potential advantages over NeRF.

Outlines

00:00

🌐 Introduction to Neural Radiance Fields

The script introduces Neural Radiance Fields (NeRFs), a technology that generates new views of scenes from RGB images using a neural network. Unlike traditional 3D reconstruction methods like point clouds, voxel grids, or meshes, NeRFs offer a continuous representation of a scene. The discussion involves a PhD student, Lewis, who explains that NeRFs use a series of RGB images to reconstruct a 3D scene, which is then used to render images from novel viewpoints. The process is akin to ray tracing but uses a neural network to determine the color and density at various points along a ray, encoding the 3D scene within the network's parameters.

05:01

🎥 Practical Application and Limitations of NeRFs

This section delves into the practical application of NeRFs, discussing the process of capturing images and training the neural network to reconstruct a 3D scene. It highlights the importance of multiple viewpoints for accurate reconstruction and the challenges of capturing a complete scene with limited camera movement. The script also touches on the downsides of NeRFs, such as the need for a substantial number of images for good reconstruction and the potential for artifacts when scenes change or are not fully captured. The conversation suggests that while NeRFs may not be as efficient as traditional rendering, they offer a powerful tool for 3D scene capture and reconstruction from real-world images.

10:01

🖼️ Exploring NeRFs with a Christmas Tree Example

The script uses a Christmas tree as an example to demonstrate the capabilities and limitations of NeRFs. It discusses the process of capturing images with a phone, the challenges of dealing with motion blur and incomplete scene capture, and the subsequent training of the NeRF. The outcome is a 3D reconstruction that looks good from certain angles but degrades in quality when viewed from outside the captured image range. The discussion points out the need for a large dataset of images for high-quality reconstruction and the potential of NeRFs to be used for creating 3D assets for games or other applications. It also mentions the existence of rival technologies like GAN splatting and the potential future of 3D rendering.

Keywords

💡Neural Radiance Fields (NeRF)

Neural Radiance Fields, or NeRF, is a technique in the field of computer vision and graphics that uses machine learning to generate high-quality 3D representations of a scene from a series of 2D images. It is a significant advancement in 3D reconstruction, as it can create detailed 3D models from simple RGB images. In the video, NeRF is the central theme, with the discussion focusing on how it reconstructs 3D scenes with high-quality details, unlike traditional methods like point clouds or meshes.

💡3D Reconstruction

3D Reconstruction refers to the process of creating a three-dimensional model of a real-world object or scene from 2D images or data. In the context of the video, 3D reconstruction is achieved through NeRF, which uses a neural network to infer the 3D structure of a scene. The script mentions traditional methods like point clouds and meshes, which are discrete and can become complex, contrasting with NeRF's ability to reconstruct scenes with high-quality detail from RGB images.

💡RGB Images

RGB images are a common type of digital image that represents color information using three color channels: red, green, and blue. In the video script, RGB images are the input data for training the NeRF model. The script explains that NeRF can reconstruct 3D scenes from these simple images, which can be captured using everyday devices like smartphones, making the technology more accessible.

💡Ray Tracing

Ray tracing is a rendering technique used in computer graphics to simulate the path of light as it interacts with objects in a 3D environment. In the video, ray tracing is compared to the process NeRF uses to determine the color and density at various points along a ray path through a scene. The script explains that while traditional ray tracing might involve bouncing light off surfaces, NeRF uses a neural network to predict these properties.

💡Overfitting

Overfitting in machine learning occurs when a model learns the training data too well, including its noise and outliers, to the point where it negatively impacts the model's performance on new, unseen data. In the video, overfitting is mentioned as a desired outcome for NeRF, as the goal is to accurately represent a specific scene from multiple angles, rather than generalize to other scenes.

💡Data Set

A data set in the context of the video refers to a collection of images and associated camera position data used to train the NeRF model. The script mentions that a good NeRF reconstruction requires a substantial number of images, ideally capturing the scene from various viewpoints. The term is used to describe the input materials that the neural network learns from to reconstruct the 3D scene.

💡Rendering

Rendering in computer graphics is the process of generating an image from a 3D model by means of computer programs. The video discusses how NeRF rendering differs from traditional rendering techniques. With NeRF, rendering involves sending out many rays and querying the neural network at various points along these rays to determine color and density, creating a detailed and accurate 3D representation.

💡Density

In the context of NeRF, density refers to a property that the neural network predicts for points along a ray path, indicating the likelihood of that point being occupied by an object in the 3D space. The script explains that density, along with color, is queried from the neural network for each point along a ray, contributing to the 3D understanding of the scene.

💡Camera Position

Camera position data is crucial for NeRF as it provides the necessary information about where each input image was taken. This data is used during the training process to help the neural network understand the spatial relationships within the scene. The video script mentions the importance of having accurate camera position data for effective 3D reconstruction.

💡Real-time Rendering

Real-time rendering refers to the ability of a system to generate 3D images or animations at the speed required for smooth, interactive viewing. The video script mentions that NeRF is not suitable for real-time rendering due to its computational complexity, which takes about 5 seconds to render a single image. This is in contrast to the expectations of real-time applications like video games or virtual reality.

Highlights

Introduction to Neural Radiance Fields (NeRF), a method for generating new views of scenes from RGB images.

NeRF's ability to reconstruct 3D scenes from simple RGB images, offering an alternative to traditional 3D reconstruction methods like point clouds and meshes.

Explanation of how NeRF works by training a neural network to reconstruct scenes in 3D from a series of RGB images.

The innovative aspect of NeRF that allows for rendering by querying a neural network for color and density at points along a ray.

Comparison of NeRF's rendering process to ray tracing, with a focus on the neural network's role in determining color and density.

The importance of capturing multiple images from different angles for effective NeRF training and reconstruction.

Discussion on the limitations of NeRF when there are changes or motion in the captured images, leading to artifacts like 'floaters'.

The practical challenge of capturing a sufficient dataset for NeRF, with a recommendation of at least 250 images for good reconstruction.

The trade-off between the quality of NeRF reconstruction and the density and diversity of the captured images.

Demonstration of how NeRF can be used to extract 3D information and potentially convert it into meshes for use in games or other 3D applications.

The current state of NeRF technology, including its limitations and the potential for future improvements.

Comparison of NeRF with other emerging techniques like GAN splatting, suggesting a competitive landscape in the field of 3D rendering.

The potential of NeRF and similar technologies to revolutionize 3D rendering and their implications for the industry.

Practical demonstration of capturing a scene with a phone and the subsequent NeRF training process.

Analysis of the quality of the NeRF reconstruction, highlighting areas of strength and weakness based on the captured data.

Discussion on the practical applications of NeRF, including its current capabilities and the potential for future development.

Transcripts

yeah it's a different kind of ner we're

looking at today so these are neural

Radiance Fields right this is this is

something that's been happening you know

been going around the AI literature for

a while a couple of years at least um

really impressive ways of generating new

views of

scenes I'm familiar with how Nerfs work

but I don't work with Nerfs every day so

I brought in my tame PhD student here

Lewis hello yeah and Lewis actually you

know trains Nerfs he's working with

Nerfs as part of his PhD and so you know

you're going to explain to us how it

works I hope so yeah what they do is

they take a series of uh RGB images and

from those RGB images of a scene they're

able to reconstruct it in 3D using a

neural network um traditional methods of

3D reconstruction are things like Point

clouds voxal grids meshes things like

that but they're discret and for some

scenarios that's all you need but for

real life situations for example this

room it can get very very complicated

many many points many many faces on your

mesh

this NE Radiance field is is able to

reconstruct it in very very high quality

detail just only using simple RGB images

that you can capture on your phone so I

think the interesting thing about this

for me is that we're actually not doing

rendering in in a way you might expect

so normally what you would do with

rendering is you'd have some meshes but

you then you know you you render them as

pixels so you rasterize the image or you

do ray tracing or something like this

with a Nerf what we're actually doing is

essentially a bit like ra tracing but

we're actually using a newal Network to

say okay this Ray is going to be this

color and this Ray is going to be this

color and so we basically building our

3D scene into the kind of inside of a

newal network so the parameters and

weights of the newal network are

actually encoding our Christmas tree or

our car or whatever or our room or

whatever else it is we're we're we're uh

looking at I'm just going to draw here a

trained Nerf for now so imagine we're

looking at this from a side angle so we

have something like this and and for the

sake of this video as you will see later

I'm going to draw you a nice Christmas

tree um I hope you're better at drawing

than me uh not a

chance that's actually a lot better than

me okay and you know I'll just shade it

in green yeah there you go so and let's

imagine that we have a camera up here

now when I say camera an image has

already been taken of of this Christmas

tree in real life and what we're doing

is simulating this this camera this

camera in real life we're an image that

looks like that makes sense looking at

it from a above sort of view so how do

we render this right so what we do we

start shooting Rays into the environment

like this this is what the neural

network does so unlike things like

diffusion which basically generate noise

isn't it the stable diffusion and yeah

yeah stuff like frogs on stilts and

stuff like that yeah th this doesn't

generate an image given a position in 3D

and a and a view Direction it will give

you a color and a density at that point

in the environment that's important

that's what is different from things

like diffusion is that it has a 3D

representation it understands the

environment so now we shot this Ray

through what we need to do is query a

series of points along this Ray and ask

the neural network okay I'm at this

point I'm looking at it from this

direction what should the color be what

should the density be so let's start off

by doing this so we're going to do this

point

here right what is the color and what is

the density we go to neural network and

it comes back and says the color is

white doesn't matter because the density

is zero why because we're in empty space

right there's nothing there's nothing

there yeah it's like air you can't

render nothing so doesn't matter let's

continue do it again nothing again

nothing again nothing again

nothing oops go thank you wow here we go

we've created a point here and it's come

back with a density of one AKA it's

inside an object and it's come out of a

color of green makes sense because we're

now entering this Christmas tree and

what we're going to do is do it again

here

here here here and you've queried all

these points along the ray the neural

networ come back with nothing nothing

Well white white white but with let's

say density 0 0

green density one green density one

green density one 0 0 perhaps the thing

that when you first learn about this is

hard to get your head around is normally

in ra tracing what you would do is you

would fire ARR into your scene usually

based on your position of your pixels

and say basically query what color is it

and it might bounce around and do

lighting or something like this but

essentially it will come back and say

yeah it's red and you paint that pixel

red in this case we're actually doing

multiple samples per Ray and we're

saying what's here what's here what's

here all the way along and what you'll

do is sometimes you'll just shoot off

and they there be nothing there

sometimes you'll hit an object you'll

intersect an object and for some time

you'll see different colors and so your

rendering process is going to be about

sending out a lot of these rays and

finding out where in 3D everything is

right as opposed to just sending out a

ray and going oh it's red yeah now that

might seem really inefficient but

actually this is the only way it trains

right because if you trained a new

network and just said yeah this is red

this is red this is green this is red

then it will work very nicely at just

drawing that particular image yeah but

you can't then move the camera over here

and say okay what does it look like from

above right where we haven't necessarily

got any images so the idea would be that

you train this with with sort of another

camera my camera's worse than yours uh

and another camera so you maybe have

three cameras but now we can draw this

one and we can draw this one and this

one because we can shoot Rays out and we

can do thing what's important here is

that when you shoot the Rays out

here they are intersecting let's say

like that that's sort of bit iffy

because it goes through there but that

how you're able to finalize where that

object is in 3D space because these

points intersect on the Rays that you

shoot which is why with Nerf one of the

downsides maybe is that you need quite a

few images for it to properly get a good

reconstruction if you only have one

image what will happen is it will look

pretty great from anywhere near that

image but if you move elsewhere and it

will it will degenerate pretty quickly

um so and another thing that's

interesting about this is normally in

any kind of AI or machine learning what

you're trying to do is generalize your

approach to some other data set so you

say I want to train on this data set but

ultimately I don't really care how well

it works on that data set CU I already

know all the answers what I care about

is how it works on this data set and

this data set but in Nerf we're actually

learning this exact scene it's not going

to reconstruct a different kind of tree

cuz that's not in the training set we

only care about producing images of this

tree from different directions yeah

overfitting to the max pretty much they

tell you never to overfit but in this

case overfit as much as as you can this

is over fitting by Design right by so I

figure we could just Trot off down the

corridor and take some pictures and have

a go now Christmas tree is actually a

really hard example of pines and

everything yeah I mean that's not an

easy thing right and and and it's a good

example in a way because we'll see some

of the problems as well as the benefits

of Nerf but also it is worth knowing

that this is something that most

reconstruction techniques are going to

really struggle on right so this is a

hard problem but it's also fun yes well

you know when you say you've got to take

quite a few pictures how many is quite a

few for something like a if you want a

good reconstruction at least 250 okay

yeah and and those are the good images

there are to be fair because Nur really

popular there's lots of research going

on and there are many many techniques

that are trying to reduce the number

they don't all work very well right so

you know your mileage May VAR if you

want really good reconstruction then a

lot of images is what you're going to

need and obviously if I'm obviously I'm

a videographer so video any good video

is good because video is a lot of I mean

aside from maybe motion blur and things

like that if you've got good quality

frames that's just more and more shots

so what have we got we've got a nice

looking Christmas tree I didn't decorate

this one and this is a very complicated

scene right because we've got bushes at

the side with huge numbers of leaves

we've got sofas we've got whatever all

this stuff is hanging off the the

trellises it's a big big place as well

one thing about normal Nerf that you see

in the literature is that they run on

fairly constrained scenes like you often

you've used a robot or some other

capture rig to capture very nice

concentric circles of images all equally

spaced everything's very constrained

we're just going to kind of wave a

around and see what happens it's more

fun so all right so Lou will catch us

some videos and then we'll uh I'll yeah

I'll stand over here and not be in the

shot what happens if you get things

changing in these images it's not good

that that's where you'll get a lot of I

think they they call them floaters where

it can't figure out where to put certain

pixels in the scene cuz they're

different throughout and then it would

just create noise so you could be photob

bombed and that would really cause a

problem exactly you get kind of a

ghostly mic

and then fing back out again that's

exactly it yeah so we've got the video

now the next step is we need to get the

camera positions and then we just got

train the Nerf and uh that should

take about an hour I've trained up this

Nerf and currently we're we're viewing

it for this I'm using Nerf Studio which

is what a lot of people are using now

cuz it's very userfriendly and it's very

very good and this is the sort of thing

that it looks like so you can see all of

the images and where they are in 3D

space and that was me going around that

Christmas tree it takes a while for the

for it the quality to increase because

what this does it renders it very quick

because you need to get an understanding

of the environment but Nerfs are slow

right for real-time rendering so it

takes about 5 seconds for it to render a

good image every time but if I move it

around here and let me just get rid of

the cameras here so you can just see the

thing give it a few seconds and it

should there we go now I want to talk

talk about what's good and what's bad

about this there are Nerf data sets out

there that are fantastic and you'll be

able to get really good high quality

reconstructions from them this is is not

so good because I took it on my phone

and there's lots of things like motion

blur different sort of issues I didn't

capture the whole scene but things like

the Christmas tree that looks pretty

good to me you know you can see the bu

balls on it and high quality you're not

this tall right with the greatest

respect if you come down yeah yeah will

it be better if you're closer to where

the original cameras were ex it should

be oh hang on hang on it's a bit finicky

mind you there we go so this is a player

yeah it's a real time renderer and it

looks good from this perspective because

this was where the images were taken

from cuz that's around my height where I

was taking it from and this is probably

very close to there you go it's very

close to where these images were taken

which is why you can see when it loads

you can see the background somewhat you

can see the tree Tre it makes sense the

Christmas tree looks good as soon as you

move out suddenly it looks pretty bad

right and that is one of the things with

Nerf is that it's very good where the

images can see the scene there is going

to be a lot of loss of quality when you

go outside where we were capturing the

images from would you call this a data

set or a picture or an image or a scene

what would you call it I would call this

a data set data set so how big is a data

set like that so this is around 300

images so Nerf data sets have a series

of Imes and then uh Json far with where

all the camera positions are that's

that's it but this is about 300 but

they're all relatively if you see here

they're all pretty close together right

if you want a really good capture really

good scene you want them spaced far

apart capturing the entire thing I'm not

Mr Fantastic with long arms I can only

capture things around here right which

is why when you go and look let's say

over here in the background

you'll see that the

background here especially on the

ceiling it's really not very good

because not a lot of dat was captured in

these images of the background so that's

why it's not as good you see here this

is just noise and the reason why that is

just noise cuz I didn't actually capture

any of that floor when I was capturing

the video I just forgot which is why

when when you look at it from here it

looks awful but that's not really the

fault of the Nerf per se it's more of a

fault of me perhaps it's worth thinking

about what we would actually use this

for because ultimately people might be

looking at this and going well it's not

as good as a 3D render but but we

actually only had to capture 300 images

we didn't have to artistically design

the tree in 3D we didn't have to paint

all the all the meshes we didn't have to

do any of that um and you know I

couldn't do that anyway because you've

seen my drawing abilities so but the

other thing is that we also as well as a

color we can also extract where the

objects are which means that you can

convert a lot of these objects into a

mesh so you can use this multi view

reconstruction to essentially obtain 3D

Volume so you could then speed up your

creation of an actual 3D asset that you

could use in a game or something like

this is this the future then of of 3D

rendering is this how it's all going to

work this month possibly yeah actually

there's a new rival to Nerf called Gan

splatting which is also providing

incredibly impressive results so maybe

that's video number

two this network is maybe slightly

better when it has a text estima the

noise so you actually put in two images

of dystopian abandoned futuristic I with

overgown plants right and then I just

put them in a four Loop and just produce

200 of them so I can pick the nice ones