nie's People
nie's Issues
Requirements
Is it possible to add a requirements.txt with working versions etc.
Example command fails with error
Hi! Thanks for sharing this code, and great paper :)
Just an FYI, I tried grabbing the code, resolving dependencies, and running the example command python3 main.py --config config/vbunny.yaml
.
It ran fine initially, but after many iterations failed with a tensor shape error:
...
Iter: 736
Iter: 737
Iter: 738
Iter: 739
Iter: 740
Traceback (most recent call last):
File "nie/main.py", line 168, in <module>
main(config)
File "nie/main.py", line 96, in main
faces.data[:f] = torch.from_numpy(np.ascontiguousarray(faces_np))
RuntimeError: The expanded size of the tensor (950000) must match the existing size (1094172) at non-singleton dimension 0. Target sizes: [950000, 3]. Tensor sizes: [1094172, 3]
I haven't tried debugging in to the issue at all, but wanted to let you know.
Does NIE work well for Inverse Rendering with calibrated RGB images?
I have read the paper, the theory is so wonderful. In "6.2 Inverse Rendering of Geometry", a single-bounce forward rendering is done with known pointlights and a known diffuse-Phong BRDF. It means that the lights and BRDF are known. And the rendered result are only 1-channel values of meshes without RGB colors.
IDR works well for calibrated RGB images without known lights and BRDF, then does NIE work well in the same case?
Fails in shapenet chair
Mitsuba3 Implementation
Hi,
I want to deploy this method into a differentiable light transport framework, such as Mitsuba3.
Could you please kindly provide some implementation in Mitsuba3?
Maybe something like the Figure9 in your article "A Theory of Topological Derivatives for Inverse Rendering of Geometry".
Thank you very much.
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