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Nerfs don’t contain a surface representation, but instead contain a occupancy field to represent the shape. This has implications for rendering speed (you gener
by hansworst 4y ago
Nerfs don’t contain a surface representation, but instead contain a occupancy field to represent the shape. This has implications for rendering speed (you generally need more samples along each ray to get an accurate representation). It also has implications for uses outside of rendering, e.g. in physics simulations where you need to know exactly where the surface is. Lastly, you can use SDFs to get arbitrarily detailed meshes by sampling the field at some resolution, who which is trickier with nerfs because the surface is more fuzzy.
- WithinReason 4y agoIn the linked video presentation [1] they automatically turn the NeRF into a mesh, so you could do the same and get faster rendering speed than SDFs. I wonder if simply enforcing less transparency towards the end of the training in a trained NeRF would help with converging to sharper object boundaries and would get the benefits of fast training without the downsides. The linked paper doesn't even discuss training times which suggests it's really bad. NeRFs a are also direction-dependent so work with specular surfaces too. [1]: https://nvlabs.github.io/instant-ngp/assets/mueller2022instant.mp4 https://nvlabs.github.io/instant-ngp/assets/mueller2022insta... (I edited my post so maybe you replied to an earlier version)