Computer Vision News Computer Vision News 4 Moving the differentiation operator under the integral and applying the product rule handles these discontinuities by splitting them into two integrals – one over the area and another over the boundary. “Unfortunately, with rasterization, we can’t sample this boundary explicitly,” Stan points out. “We have a fixed grid of pixels, and that’s our samples. We can’t decide to sample more or decide to sample on the boundary.” Some methods attempt to address this challenge by relaxing the problem – substituting the original, more complex issue with an easier, more manageable one. A notable example is soft rasterization, which replaces the original triangles in a mesh with triangles with blurry edges to eliminate discontinuities. However, this introduces other issues, as it is not what you wanted to render and adds cost. The closest method to this work is Nvdiffrast, which uses differentiable analytic antialiasing. It is fast but produces sparse gradients and modifies the rendered image. “In our case, we took a slightly different direction, so we don’t modify the rendered image, but we can still backpropagate the gradients,” Stan explains. “We have this discontinuous rasterization, but we assume there is another fully continuous process that produces the exact same result as we get with our initial non-differentiable rasterization.” The novelty of this work lies in the introduction of micro-edges. In this virtual construct, edges are placed precisely between pixels so that when rendered with an imaginary, fully-antialiased renderer, images appear identical to the vanilla nondifferentiable rasterization. What this means is that we can assume the ECCV Best Paper Hon. Mention
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