neural-implicit-queries
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outward_normal
In the function outward_normal
in render.py
, when method == 'finite_differences'
the gradient is calculated by tetrahedron
algorithm.
This is suitable for SDF, but how about Occupancy Function?
Is there any theoritical background that it is suitable for Occupancy Function too under some conditions? (for example, if the tetrahedron is small enough and the Occupancy Function is implemented on MLP?)
Or, this outward_normal
is not recommended to use for Occupancy Function?