Comments (2)
Hi @kwea123, thanks for your interest in our work! We don't have full benchmarks yet but that's on our TODO list in the coming weeks along with PSNR visualization on the GUI.
The much slower training time compared to instant-ngp
is very much expected; our code is mostly PyTorch based and selectively CUDA-optimized, although there's a few places we plan to optimize for better results.
Just looking at your attached screenshots though there's something you can adjust to increase the visual quality. Right now the sampling of points to integrate along the ray is selected based on the near and far plane, and your far plane on the screenshot is set to 100. Decreasing this will significantly improve results... or alternatively you can choose the exact camera (with the near and far plane used in training) by choosing a camera on the GUI.
Happy to answer whatever other questions you might have, or even discuss. 😁
from kaolin-wisp.
For reference, here's an image after similar number of epochs as your screenshot.
from kaolin-wisp.
Related Issues (20)
- BLAS: Support for bitgrid & DDA marcher
- New docsite
- New configuration system
- Broken pages and redirects
- Tracer / NeF: background values for additional channels
- Interactive issue HOT 1
- Larger RAM usage with the new config system HOT 4
- nglod does not save the weights+model HOT 1
- Loading multiple pipelines and trainers HOT 5
- closest_point function is not implemented HOT 4
- White fog during the app running HOT 2
- Impossible to use GUI HOT 1
- Missing uint typedef
- Initialize OctreeGrid with SPC Features
- RuntimeError: expected scalar type Double but found Float
- Where can I find some doc of how to use this research library HOT 1
- Invalid configuration choice for "blas" HOT 4
- trying to install v0.1.1
- `uniform_sample_cuda` fails when `spc_render.unbatched_raytrace` return empty tensors for `ridx`, `pidx`, and `depth` HOT 1
- `--tracer.raymarch-type voxel` uses too much VRAM, which triggers OutOfMemoryError
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