[HN Gopher] Sparse Voxels Rasterization: Real-Time High-Fidelity...
___________________________________________________________________
Sparse Voxels Rasterization: Real-Time High-Fidelity Radiance Field
Rendering
Author : jasondavies
Score : 111 points
Date : 2025-02-21 21:06 UTC (1 days ago)
(HTM) web link (svraster.github.io)
(TXT) w3m dump (svraster.github.io)
| atilimcetin wrote:
| I think this paper is as important as original Gaussian Splatting
| paper.
| mitthrowaway2 wrote:
| Why do you say so?
| aaroninsf wrote:
| 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
| <thing>, 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?
| corysama wrote:
| > 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.
| vessenes wrote:
| 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.
| davikr wrote:
| What is the usecase for radiance fields?
| corysama wrote:
| 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.
| loxias wrote:
| 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.
| bondarchuk wrote:
| > _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?
| chpatrick wrote:
| There's no neural net with gaussian splatting, it's a fancy
| pointcloud that's optimized with ML techniques.
| bondarchuk wrote:
| I know, that's the point.
| chpatrick wrote:
| Mea culpa, I misunderstood.
| bondarchuk wrote:
| Funny, it almost sounds like a straight efficiency improvement of
| Plenoxels (the direct predecessor of gaussian splatting), which
| would mean gaussian splatting was something of a a red
| herring/sidetrack. Though I'm not sure atm where the great
| performance gain is. Definitely interesting.
| ibrarmalik wrote:
| How is plenoxels a direct predecessor of gaussian splatting?
| yyeboah wrote:
| They both emerged out of the pursuit of a more efficient
| solution for addressing the inefficiencies in NeRF, which was
| mainly due to expensive ray marching and MLP calls. Before
| the emergence of Gaussian splatting, grids, such as plenoxels
| were all the rage. Of course, Gaussian splatting here refers
| to the paper, "3D Gaussian Splatting for Real-Time Radiance
| Field Rendering"
| HexDecOctBin wrote:
| Why is this called rendering, when it would be more accurate to
| call it reverse-rendering (unless "rendering" means any kind of
| transformation of visual-adjacent data)?
| yorwba wrote:
| The reverse-rendering is not real-time, but takes several
| minutes. Only rendering new viewpoints from the resulting
| sparse voxel representation runs at high enough framerates.
| markisus wrote:
| This is basically Gaussian splat using cubes instead of
| Gaussians. The cube centers and sizes choices are discrete and
| non overlapping, hence the name "sparse voxel". The qualitative
| results and rendering speeds are similar to Gaussian splat, and
| it's sometimes better or worse depending on the scene.
___________________________________________________________________
(page generated 2025-02-22 23:01 UTC)