You're talking like you worry the audiences will understand your paper
@dylanSmith-r5h Жыл бұрын
the project link is currently unavailable. Could you please share it again? Thank you very much.
@UTKARSHTIWARI-v4o Жыл бұрын
what app is being used to capture images in the correct position/ at 1:30
@FredPauling2 жыл бұрын
The paper that started a revolution.
@alhdlakhfdqw2 жыл бұрын
really awesome video thank you so much! great work :)
@user-oj4hr5rh6i2 жыл бұрын
😮
@xiaoyanqian68982 жыл бұрын
Hi, thank you for the great work. I just wonder what software you used to make this video that could vividly show the iterations, the Fourier features and its Std, frequencies, and reconstruction.
@brainlink_2 жыл бұрын
Thank you so much for this wonderful video!
@brainlink_2 жыл бұрын
Thank you so much for this wonderful video!
@yifeipei54842 жыл бұрын
You can just use Discrete Cosine Transform to do it. We have a paper: www.cse.scu.edu/~yliu1/papers/ISCAS2020Yifei.pdf
@TheMcSebi2 жыл бұрын
Stunning
@sapiranimations2 жыл бұрын
Would it be feasible to somehow incorporate the fourier features in the activation functions? So that the entire model can be made high frequency sensitive instead of just the input
@yifeipei54842 жыл бұрын
You can just use Discrete Cosine Transform to do it. It's much simpler. No need to use Fourier transform to make it complex. We have a paper: www.cse.scu.edu/~yliu1/papers/ISCAS2020Yifei.pdf You can write 2D-DCT into 1 dimensional representation for activation function. See our another paper: www.cse.scu.edu/~yliu1/papers/ISCAS2021YifeiPei.pdf
@yifeipei54842 жыл бұрын
However, these transforms can only work on fully-connected neural networks. It gives bad results on CNN.
@pratik2453 жыл бұрын
Such a new concept.. No body really thought about it before them. How genious!!
@BenEncounters3 жыл бұрын
Could this technique be combined with photogrammetry to be able to represent reflective and plain surfaces as well as people?
@snowjordan68223 жыл бұрын
Wow
@sheevys3 жыл бұрын
How does it compare to NeRF?
@alexeychernyavskiy41933 жыл бұрын
Great video, especially the part with the scale is well explained
@Benjamin-il8vf4 жыл бұрын
Good channel. Looking forward to seeing more from you! By the way, go and search for SMZeus . c o m!! It will help you promote your videos!!
@alfcnz4 жыл бұрын
Nice work and good presentation. Just a couple of questions, though. At 5:59 you define the loss as using the function “render” which has never been defined here or on the paper. Can you please clarify where it comes from? Thanks. Also, at 4:46 I understand that C = 𝔼(c), with c vectorial output of the net, following the cumulative opacity, function of σ, output of the network. Is this correct?
@oOXpycTOo3 жыл бұрын
I guess, render function means just computing the color of an image at a particular pixel, using the formula, defined in kzbin.info/www/bejne/goOkopiDbaqdhdE
@crazyfox554 жыл бұрын
A+ in my book.
@ChrrZ4 жыл бұрын
incredible!!
@othoapproto96034 жыл бұрын
a solution looking for a home, Rick Deckard would approve
@zhenyujiang95744 жыл бұрын
At 9:07, the reflection on the TV screen is also changing accordingly. That's really amazing!
@МаксимЗубков-ь4ъ Жыл бұрын
what did you expect, it was present in the input images. for the code, there should be no difference between the reflection and the 2nd room behind the tv
@nikronic4 жыл бұрын
You might not believe, but 6 hours ago, I suddenly decided to read your amazing work! now I am here! Thank you
@jcjensenllc4 жыл бұрын
In about the fourth set of samples there is a dinosaur skull ( on bottom row) that had serious flaws. Please explain why that one failed.
@importon4 жыл бұрын
Is there a google colab notebook we can try this out with?
@jkickass4 жыл бұрын
ok but what am i going to do with wobbly 3d images?
@jonathanl27574 жыл бұрын
Please add vertical capture to fyuse for those of us who are passionate about our captures!
@chasemarangu5 жыл бұрын
wow
@DenisVostrikov5 жыл бұрын
Impressive demonstration of mathematics knowledge. But it is more practical to shoot video for the best result. Isn't it?
@user-vh9kw6gc9y5 жыл бұрын
the matrix had always been, the only possible goal
@Veptis5 жыл бұрын
Can you capture input data from a video? Would this work with non RGB images? Monochrome or even something like low resolution thermal imaging (320*240)
@mauriciopereira48245 жыл бұрын
Empolgante! : )
@bruce_luo5 жыл бұрын
Wow. And this video itself stands out for its quality too.
@polytrauma1015 жыл бұрын
Very promising results, great work! Is this method potentially applicable to 360° lightfields?
@andrews40305 жыл бұрын
is it possible to catch instant processes by this method, I mean splash of water or fire??
@gregkrazanski5 жыл бұрын
i assume if you had 25 cameras in a semi-regular grid that all took a photo instantly, it would work
@lilboi30005 жыл бұрын
This is so awesome!!!! Also didn't realize you were at Berkeley until I saw that fern, that's VLSB isn't it haha?
@games5285 жыл бұрын
Make this into a phone app and become rich.
@Geddy1355 жыл бұрын
any chance on someone making an app for this? or even just a more streamlined program to use on desktops?
@Wiiplay1235 жыл бұрын
Really wish there was a Windows port :\
@amoose1365 жыл бұрын
Possibly as the code is open source and has 100+ stars. Keep in mind it’s a remote possibility and all of these tools typically aren’t even shared.
@RivenbladeS5 жыл бұрын
In what subjects is these techniques useful? What do they solve and where will you use them?
@Wiiplay1235 жыл бұрын
@@RivenbladeS They didn't really go over the uses of light fields in the video, but it's really good for VR kzbin.info/www/bejne/hYa4Y6x9fcyDh7s
@jonathanl27574 жыл бұрын
Fyuse app is a limited implementation
@gaussianguaicai5 жыл бұрын
It’s soo cool to capture local light field by using just a mobile phone , I tried the google spherical light field before, clearly this has the quality to compere it.
@shrillcacophony5 жыл бұрын
Outstanding! Have there been any explorations into how your method would work with camera arrays--perhaps including video camera arrays?
@benmildenhall31695 жыл бұрын
It works very well on the 5x5 camera array data collected by the authors of Soft3D (ericpenner.github.io/soft3d/). We have not found any public video camera array data to try it on yet, theoretically it should work, though practically the size of the output MPIs might become an issue when reconstructing more than 1-2 seconds worth of data.
@infiniterealities4D5 жыл бұрын
@@benmildenhall3169 Incredible work. We might be able to supply genlocked high-resolution video data. Can you work with larger arrays? What image resolution would you prefer? And do you have any technical preferences for camera spacing and orientation?
4 жыл бұрын
@@infiniterealities4D Wow the fact that IR sees potential in that means it's really as clean of a technic as I thought.