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wanmeihuali
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taichi_3d_gaussian_splatting/GaussianPointCloudRasterisation.py
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| alpha_grad_from_depth = (T_i * (depth_i-d_pixel) + 1.0/(1.0-alpha) * \ | ||
| (depth_w_i - acc_alpha_w_i* d_pixel)) / \ | ||
| (org_accumulated_alpha+0.00001) * pixel_depth_grad | ||
| alpha_grad_from_accumulated_alpha = (T_i- 1.0/(1.0-alpha) * acc_alpha_w_i) * _accumulated_alpha_grad |
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Please format your code. I haven't looked deep into your formula so I'm not sure if it is correct, but for implementation, it seems to be the same as your formula. Have you gotten it to converge on alpha in some more complex tests? e.g. as an extra loss?
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Formated the code, hopefully it's now easier to understand.
Have you gotten it to converge on alpha in some more complex tests? e.g. as an extra loss?
What do you mean as an "extra loss"? Do you mean train with RGB and depth loss together?
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Test depth grad on xyz and alpha.
Note: I found the initialization of alpha is important:
Results: Optimize pts alpha only (red are all points, blue are points with high alpha,)
Results: Optimize pts coords only
