3D Models at City Scale!

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Louis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

Last year we saw NeRF, NeRV, and other networks able to create 3D models and small scenes from images using artificial intelligence. Now, we are taking a small step and generating a bit more complex models: whole cities. Yes, you’ve heard that right, this week’s paper is about generating city-scale 3D scenes with high-quality details at any scale. It works from satellite view to ground-level with a single model. How amazing is that?! We went from one object that looked okay to a whole city in a year! What’s next!? I can’t even imagine.

The model is called CityNeRF and grows from NeRF, which I previously covered on my channel. NeRF is one of the first models using radiance fields and machine learning to construct 3D models out of images. But NeRF is not that efficient and works for a single scale. Here, CityNeRF is applied to satellite and ground-level images at the same time to produce various 3D model scales for any viewpoint. In simple words, they bring NeRF to city-scale. But how? Watch the video to learn more!

Video Transcript

00:00

last year we first saw nerf then nerve

00:03

and other networks able to create 3d

00:05

models and small scenes from images

00:07

using artificial intelligence now we are

00:09

taking a small step and generating a bit

00:12

more complex models whole cities yes

00:15

you’ve heard that right this week’s

00:16

paper is about generating city scale 3d

00:19

scenes with high quality details at any

00:21

scale it works from satellite view to

00:23

ground level with a single model how

00:26

amazing is that we went from one object

00:28

that looked ok to a whole city in a year

00:31

what’s next i can’t even imagine but i

00:33

can easily imagine what should be next

00:35

for you your next step as an ai

00:37

professional or student should be to do

00:39

like me and try the sponsor of today’s

00:41

episode weights and biases if you run a

00:44

lot of experiments such as playing with

00:45

guns or any models like this one you

00:48

should be using weights and biases it

00:49

made my life so much easier you have no

00:52

idea and it takes not even five minutes

00:54

to set up simply install and import it

00:56

into your code add a line to initialize

00:58

and another to say which metric to track

01:01

and voira you will have all of your

01:03

future experiments in a project where

01:05

you can see all of the input hyper

01:07

parameters output matrix and any

01:09

insights that you and your team have and

01:11

easily compare all of them to find out

01:13

what worked best you can help out the

01:15

channel and give it a try with the first

01:17

link below it’s completely free for

01:19

personal use and i promise it will be

01:21

set up in under 5 minutes the model is

01:23

called city nerf and grows from nerf

01:26

which i previously covered on my channel

01:28

nerf is one of the first models using

01:30

radeon’s fields and machine learning to

01:32

construct 3d models out of images but

01:35

nerf is not that efficient and works for

01:37

a single scale here city nerf is applied

01:40

to satellite and ground level images at

01:42

the same time to produce various 3d

01:44

model scales for any viewpoint in simple

01:47

words they bring nerf to city scale but

01:50

how i won’t be covering how nerf works

01:52

since i’ve already done this in a video

01:54

you can see in the top right corner of

01:56

your screen right now if you haven’t

01:57

heard of this model yet instead i’ll

01:59

mainly cover the differences and what

02:02

city nerf brings to the initial nerf

02:04

approach to make it multiscale here

02:06

instead of having different pictures a

02:08

few centimeters apart they have pictures

02:10

from thousands of kilometers apart

02:12

ranging from satellites to pictures

02:14

taken on the road as you can see north

02:16

alone fails to use such drastically

02:19

different pictures to reconstruct the

02:20

scenes in short using the weights of a

02:23

multi-layer perception a basic neural

02:25

network nerf will process all images

02:27

knowing their viewpoint and positions in

02:30

advance nerf will find each pixel’s

02:32

colors and density using array from the

02:35

camera so it knows the camera’s

02:37

orientations and can understand depth

02:39

and corresponding colors using all the

02:42

arrays together then this process is

02:44

optimized for the convergence of the

02:46

neural network using a loss function

02:48

that will get us closer to the ground

02:50

truth while training which is the real

02:52

3d model that we are aiming to achieve

02:55

as you can see here the problem is that

02:57

the quality of the rendered scene is

02:59

averaged at the most represented

03:01

distances and makes specific viewpoints

03:03

look blurry especially because we

03:05

typically have access to much more

03:07

satellite imagery than close views we

03:09

can try to fix this by training the

03:11

algorithm with different skills

03:12

independently but as they explain it

03:14

causes significant discrepancies between

03:16

successive scales so you will not be

03:19

able to zoom in and have a fluid nice

03:21

looking 3d scene at all times instead

03:24

they train their model in a progressive

03:26

manner meaning that they are training

03:27

their model in multiple steps

03:29

independently where each new step starts

03:31

from the learned parameters of the

03:33

previous step these steps are for

03:35

specific resolutions based on the camera

03:38

distance from the object of interest

03:40

here demonstrated with l so each step

03:42

will have its pre-processed pack of

03:45

images to be trained on and further

03:47

improved by the following steps starting

03:49

from far satellite images to more and

03:52

more zoomed in images the model can add

03:54

details and make a better foundation

03:56

over time as shown here they start by

03:59

training the model on l1 their farthest

04:01

view and end up with the ground level

04:03

images always adding to the network and

04:05

fine-tuning the model from the learn

04:07

parameters step to different scales so

04:09

this simple variable l controls the

04:12

level of detail and the rest of the

04:14

model stays the same for each stage

04:16

compared to having a pyramid-like

04:18

architecture for each scale as we

04:20

typically see the rest of the model is

04:22

basically an improved and adapt version

04:24

of nerf for this task you can learn more

04:26

about all the details of the

04:27

implementations in differences with nerf

04:30

in their great paper linked in the

04:31

description below and the code will be

04:33

available soon for you to try it if

04:35

interested and voila this is how they

04:38

enable nerf to be applied to city scale

04:40

scenes with amazing results it has

04:42

incredible industrial potential and i

04:44

hope to see more work in this field soon

04:47

thank you for watching and if you are

04:48

not subscribed please consider clicking

04:50

on the little red button it’s free and

04:53

you will learn a lot i promise and i

04:55

will be sharing a couple of special

04:56

videos for the end of the year stay

04:58

tuned

05:00

[Music]         

References

►Read the full article: https://www.louisbouchard.ai/citynerf/
►Xiangli, Y., Xu, L., Pan, X., Zhao, N., Rao, A., Theobalt, C., Dai, B.
and Lin, D., 2021. CityNeRF: Building NeRF at City Scale. https://arxiv.org/pdf/2112.05504.pdf
►Project link: https://city-super.github.io/citynerf/
►Code (coming soon): https://city-super.github.io/citynerf/
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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