Oh it tries to capture the entire scene in one model? I didn't expect that.
Next gen lighting technologies - voxelised, traced, and everything else *spawn*
- Thread starter Scott_Arm
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From my findings, current NRC doesn't seem to properly cache narrow occluded area. Hope it will be fixed.
Do they have a reference path Traced to See how it should Look in ground truth? It would be nice If they did.
Also AFAIK, NRC should also do specular bounces. You See good evidence of that?
Ext3h
Regular
Is SHaRC spatial only, or direction aware? Asking because the demo case appeared to have painfully avoided geometry where different lighting conditions would have applied to the front/back facing side a geometry thinner than the voxel size.
Simply didn't bother yet to take a look at the actual hash function, so did someone already check that?
I do suspect it doesn't, so you'd always get an unintended bleed through thin occluders for the parts of GI which had not been sampled in screen space.
Simply didn't bother yet to take a look at the actual hash function, so did someone already check that?
I do suspect it doesn't, so you'd always get an unintended bleed through thin occluders for the parts of GI which had not been sampled in screen space.
DavidGraham
Veteran
Alex was wondering if DDGI is still there, it is.
It has 3 techniques tight now, NRC which uses Tensor cores, SHaRC and DDGI use compute.
wccftech.com
It has 3 techniques tight now, NRC which uses Tensor cores, SHaRC and DDGI use compute.
NVIDIA RTXGI 2.0 Now Available: The Next Frontier of Ray Traced Visuals With Neural Radiance Cache, Spatial Hash Radiance Cache & Dynamic Diffuse Global Illumination Support
NVIDIA updated its ray tracing global illumination SDK to the latest feature set in RTXGI 2.0, offering support for new technologies.
wccftech.com
Yes. More detailed images I took with sample accumulation on and denoiser off:
I think it does.
Perhaps the 'voxel' in SHaRC is not a common word meaning 'volume pixel'.
I'd be surprised if they aren't using surfels for SHaRC, that will handle two sides just fine. The Neural net will find it difficult to learn surface orientation from just the sparse samples AFAICS.
I think they will have to compromise on the giant neural model approach and do something hybrid.
PS. oh, it's really voxels. This seems a lot less advanced than Dice's surfel GI.
I think they will have to compromise on the giant neural model approach and do something hybrid.
PS. oh, it's really voxels. This seems a lot less advanced than Dice's surfel GI.
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The tensor cores are there any way, though trying to capture the radiance cache in one model is a bit silly if that's still what they are doing. That feels a bit like DLSS 1, a very sloppy way to satisfy a mandate to leverage the tensor cores.
MLPs/SVMs can serve as a way to compress and interpolate between non-uniform samples, but it makes more sense to do that localized in a scene for instance for a surfel hemisphere or for prefiltered view dependent voxels.
Ext3h
Regular
SHARC/shaders/Include/HashGridCommon.h at 180f825f5fbae03aaaa593727d80d1094f913f00 · NVIDIAGameWorks/SHARC
Spatial Hash Radiance Cache (SHaRC) Library . Contribute to NVIDIAGameWorks/SHARC development by creating an account on GitHub.
github.com
Didn't use surfels. Just a subdivision of the voxel into 8 axis aligned primary normal orientations. And it's simply recording voxels in world coordinates. With no extra metadata for depth, radius or anything, nor even the option to dynamically increase resolution where potentially needed.
So it does suffer from an well known issue:
Also didn't properly account for hash collisions! So this will occasionally resolve to completely wrong hash entries. Partially self inflicted issue, as it used the same bits of entropy for both the bucket and key inside the bucket, could have further reduced the chance of collisions at the same space requirements significantly.
And don't get me started on the complete lack of any locality. It's 32bit random reads all over the place.
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