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>There’s little reason to believe that optimizing an SDF and training a NeRF are any different in terms of optimization speed or resolution A trainable SDF rep

>There’s little reason to believe that optimizing an SDF and training a NeRF are any different in terms of optimization speed or resolution A trainable SDF representation could very well be slower to train than a trainable NeRF representation > If all you want is the geometry, then material properties aren’t an advantage, and could add unnecessary complication, right? Unless the SDF's inability to model view dependence would interfere with its ability to minimise its loss

dahart 4y ago
> 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.