Re:Differentiable Signed Distance Function Rendering
by dahart on Monday May 09, 2022 @12:26PM
(#31316167)
> A trainable SDF representation could very well be slower to train than a trainable NeRF representation
Sure. It could very well be faster too (or the same-ish, if it turns out they’re more fundamentally the same than different). Carrying view dependent data around is more bandwidth, potentially significantly more, depending on how you model it. How you model it is under development, and a critical part of the question here.
This all depends on a whole bunch of details that are not settled and can have many implementations. There is significant overlap between the ideas in the Nvidia paper and the EPFL paper, and it’s worth being a bit more careful about defining exactly what it is we’re talking about. It’s easy to say one might be faster. It’s harder to identify the core concepts and talk about what properties are intrinsic to these ideas over a wide variety of implementations, and how they actually differ at their core.
Sure. It could very well be faster too (or the same-ish, if it turns out they’re more fundamentally the same than different). Carrying view dependent data around is more bandwidth, potentially significantly more, depending on how you model it. How you model it is under development, and a critical part of the question here.
This all depends on a whole bunch of details that are not settled and can have many implementations. There is significant overlap between the ideas in the Nvidia paper and the EPFL paper, and it’s worth being a bit more careful about defining exactly what it is we’re talking about. It’s easy to say one might be faster. It’s harder to identify the core concepts and talk about what properties are intrinsic to these ideas over a wide variety of implementations, and how they actually differ at their core.