Computer Vision News Computer Vision News 6 He recalls an example of a human head reconstruction. Here, subtle details, like a tongue accidentally penetrating the teeth during specific frames, are nearly impossible to optimize without properly handling geometry intersections. The error might be small, just one or two millimeters, but it significantly affects realism. In scenarios like this, a differentiable renderer that cannot backpropagate gradients effectively would fail to correct the error. “The naive, straightforward way to handle it would be, before each optimization iteration, we could go through the geometry, detect intersecting triangles, split them, and now we don’t have geometry that has intersections,” he points out. “Still, this operation has to be differentiable, and that is costly. In my case, with zero overhead, I add the possibility to optimize intersecting geometry.” While there may be other ways to solve this problem, Stan’s research represents one step in a broader exploration of differentiable rendering techniques. Looking ahead, he hopes it will inspire others to build upon it, pushing the field forward toward more efficient and principled methods. “Maybe someone can come up with something as fast as rasterization but more principled,” Stan ponders. “There are many possibilities. I compare with ray tracing methods like Mitsuba and Redner. Those are principled ray tracing packages that use ray tracing to render meshes and compute global illumination and shadows. I’m just doing rasterization. The shading afterward is deferred. I use a neural network in most applications to perform the shading in screen space.” Stan has been developing this method for several years, having initially had ECCV Best Paper Hon. Mention
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