> Meshes and bezier patches are boundary representations with no information of the volume they enclose
Pedantic on my part, but SDF is still just a surface/boundary representation at the end of the day. It may have additional benefits that you point out such as quickly computing if you're inside/outside or being able to more easily deform the shape (through any number of procedures including slicing), but you'd probably use a density grid instead of a SDF if you're dealing with anything volumetric, such as clouds or smoke.
> You don't. I didn't notice render times, but "interactive frame rates" would be lacking.
Yeah definitely, poor wording on my part.
> Most SDF primitives and their compositions are not analytic (from use of abs, fract, etc). Differentiation by finite differences is most common. Few are using automatic differentiation. I don't quite follow how back prop would produce the surface gradient, but I doubt it would be faster than these methods.
I don't think we're talking about the same thing, I'm not referring to the surface gradient when I say differentiable rendering.
Both differentiable rendering with an SDF (i.e. SDFDiff https://arxiv.org/pdf/1912.07109.pdf) and with a neural SDF(DIST http://b1ueber2y.me/projects/DIST-Renderer/) use automatic differentiation to compute the gradient of the rendering/image-generating process. Section 3.4 in the DIST paper discusses where back prop comes into all of this. Basically your surface is defined by the weights of some neural net (i.e. a neural SDF) and you need to know the gradient of your image with respect to those weights.
For a variety of reasons, these surface representations are easier to handle than triangle meshes which break certain desirable properties for differentiation and require extra care as a result (see edge sampling https://people.csail.mit.edu/tzumao/diffrt/ for an early example of the challenges of doing differentiable rendering with a triangle mesh).
Pedantic on my part, but SDF is still just a surface/boundary representation at the end of the day. It may have additional benefits that you point out such as quickly computing if you're inside/outside or being able to more easily deform the shape (through any number of procedures including slicing), but you'd probably use a density grid instead of a SDF if you're dealing with anything volumetric, such as clouds or smoke.
> You don't. I didn't notice render times, but "interactive frame rates" would be lacking.
Yeah definitely, poor wording on my part.
> Most SDF primitives and their compositions are not analytic (from use of abs, fract, etc). Differentiation by finite differences is most common. Few are using automatic differentiation. I don't quite follow how back prop would produce the surface gradient, but I doubt it would be faster than these methods.
I don't think we're talking about the same thing, I'm not referring to the surface gradient when I say differentiable rendering.
Both differentiable rendering with an SDF (i.e. SDFDiff https://arxiv.org/pdf/1912.07109.pdf) and with a neural SDF(DIST http://b1ueber2y.me/projects/DIST-Renderer/) use automatic differentiation to compute the gradient of the rendering/image-generating process. Section 3.4 in the DIST paper discusses where back prop comes into all of this. Basically your surface is defined by the weights of some neural net (i.e. a neural SDF) and you need to know the gradient of your image with respect to those weights.
For a variety of reasons, these surface representations are easier to handle than triangle meshes which break certain desirable properties for differentiation and require extra care as a result (see edge sampling https://people.csail.mit.edu/tzumao/diffrt/ for an early example of the challenges of doing differentiable rendering with a triangle mesh).