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Sparse Voxels Rasterization: Real-Time High-Fidelity Radiance Field Rendering

svraster.github.io

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Re: Sparse Voxels Rasterization: Real-Time High-Fidelity Radiance Field Rendering

#3

Post by aaroninsf »

Can someone ELI5 what the input to these renders is?

I'm familiar with the premise of NeRF "grab a bunch of relatively low resolution images by walking in a circle around a subject/moving through a space", and then rendering novel view points,

but on the landing page here the videos are very impressive (though the volumetric fog in the classical building is entertaining as a corner case!),

but I have no idea what the input is.

I assume if you work in this domain it's understood,

"oh these are all standard comparitive output, source from , which if you must know are a series of N still images taken... " or "...excerpted image from consumer camera video while moving through the space" and N is understood to be 1, or more likely, 10, or 100...

...but what I want to know is,

are these video- or still-image input;

and how much/many?

Re: Sparse Voxels Rasterization: Real-Time High-Fidelity Radiance Field Rendering

#4

Post by corysama »

aaroninsf wrote: post #3

Can someone ELI5 what the input to these renders is? I'm familiar with the premise of NeRF "grab a bunch of relatively low resolution images by walking in a circle around a subject/moving through a space", and then rendering novel view points, but on the landing page here the videos are very impressive (though the volumetric fog in the classical building is entertaining as a corner case!), but I have no idea what the…

> We optimize adaptive sparse voxels radiance field from multi-view images…

Pretty sure the input is the same as for NeRFS, GS and photogrammetry: as many high rez photos from as many angles as you have the patience to collect.

I think the example scenes are from a common collection of photos that are being widely used as a common reference point.

Re: Sparse Voxels Rasterization: Real-Time High-Fidelity Radiance Field Rendering

#6

Post by vessenes »

aaroninsf wrote: post #3

Can someone ELI5 what the input to these renders is? I'm familiar with the premise of NeRF "grab a bunch of relatively low resolution images by walking in a circle around a subject/moving through a space", and then rendering novel view points, but on the landing page here the videos are very impressive (though the volumetric fog in the classical building is entertaining as a corner case!), but I have no idea what the…

They are photos, in this case from the MIP Nerf 360 dataset. I believe there are on the order of hundreds per scene. They are not videos turned into photos. Some datasets include high grade position and directional information -- I believe this dataset does not, so you need to do some work to orient the rendering training. But, I'm a hobbyist, so all this could be very wrong.

Re: Sparse Voxels Rasterization: Real-Time High-Fidelity Radiance Field Rendering

#7

Post by loxias »

I look forward to reading this in closer detail, but it looks like they solve an inverse problem to recover a ground truth set of voxels (from a large set of 2d images with known camera parameters), which is underconstrained. Neat to me that it works w/o using dense optical flow to recover the structure -- I wouldn't have thought that would converge.

Love this a whole heck of a lot more than NeRF, or any other "lol lets just throw a huge network at it" approach.

Re: Sparse Voxels Rasterization: Real-Time High-Fidelity Radiance Field Rendering

#9

Post by corysama »

davikr wrote: post #5

What is the usecase for radiance fields?

Take a bunch of photos of an object or scene. Fly around the scene inside a computer.

https://news.ycombinator.com/item?id=43120582

Like photogrammetry. But, handles a much wider range of materials.

Re: Sparse Voxels Rasterization: Real-Time High-Fidelity Radiance Field Rendering

#10

Post by bondarchuk »

loxias wrote: post #7

I look forward to reading this in closer detail, but it looks like they solve an inverse problem to recover a ground truth set of voxels (from a large set of 2d images with known camera parameters), which is underconstrained. Neat to me that it works w/o using dense optical flow to recover the structure -- I wouldn't have thought that would converge. Love this a whole heck of a lot more than NeRF, or any other "lol l…

>Love this a whole heck of a lot more than NeRF, or any other "lol lets just throw a huge network at it" approach.

Well yes, but that's what gaussian splatting also was. The question is: are their claims to be so much better than gsplat accurate?

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