My understanding on the beta in the NeRF-w is computed independently for each location, and also rendered with similar alpha-compositing weights. I found similar code in your repo in the models/rendering.py file line 176
results['beta'] = reduce(transient_weights*transient_betas, 'n1 n2 -> n1', 'sum')
However, since the paper claims beta as the variance of each location, should the corresponding scaling coefficient be the square of the alpha-compositing weights?
results['beta'] = reduce(transient_weights*transient_weights*transient_betas, 'n1 n2 -> n1', 'sum')
I might made a mistake, and I really look forward to your reply.
Thanks in advance for all the help you can provide.
Hi,
Thank you for contributing this repo.
My understanding on the beta in the NeRF-w is computed independently for each location, and also rendered with similar alpha-compositing weights. I found similar code in your repo in the models/rendering.py file line 176
results['beta'] = reduce(transient_weights*transient_betas, 'n1 n2 -> n1', 'sum')
However, since the paper claims beta as the variance of each location, should the corresponding scaling coefficient be the square of the alpha-compositing weights?
results['beta'] = reduce(transient_weights*transient_weights*transient_betas, 'n1 n2 -> n1', 'sum')
I might made a mistake, and I really look forward to your reply.
Thanks in advance for all the help you can provide.