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Thanks for the great work. Does your code allow taking covariance matrix as input? If I set "cov3D_precomp" to True, does the function build_covariance_from_scaling_rotation give me exactly the same operations as the ones in your cuda code if scales and rotations are given as input instead?
Thanks in advance
The text was updated successfully, but these errors were encountered:
I haven't tested cov3D_precomp , but it should work for that. But keep in mind that the cov3D_precomp should be a 3x3 homogeneous transformation matrix,
defsetup(means3D, scales, quats, viewmat, projmat):
rotations=build_scaling_rotation(scales, quats).permute(0,2,1)
p_view= (means3D @ viewmat[:3,:3]) +viewmat[-1:,:3]
uv_view= (rotations @ viewmat[:3,:3])
M=torch.cat([homogeneous_vec(uv_view[:,:2,:]), homogeneous(p_view.unsqueeze(1))], dim=1)
T=M @ projmat# T stands for (WH)^T in Eq.9returnT
I just need to check to the consistency between cuda and pytorch then.
Hi, @Shubhendu-Jena , can you check this PR #27 to see if the current implementation works for you?
I have tested on a simple experiments show the precomp yields identical results to the previous. I tested on the data nerf-synthetic chair, without regularizations, using an RTX 3090.
EXP
PSNR
Time
previous
35.29
12 min
new
35.35
12 min
new (pre_comp)
35.35
21 min
BTW, make sure you recompile the latest rasterizer.
Hi,
Thanks for the great work. Does your code allow taking covariance matrix as input? If I set "cov3D_precomp" to True, does the function build_covariance_from_scaling_rotation give me exactly the same operations as the ones in your cuda code if scales and rotations are given as input instead?
Thanks in advance
The text was updated successfully, but these errors were encountered: