![]() ![]() We show superior reconstruction quality on a set of synthetic and real-world translucent objects as compared to previous methods that model only surface reflection. We validate our derivatives by comparing against finite differences and demonstrate the effectiveness of our technique by comparing inverse-rendering performance with previous methods. This efficiently avoids potential bias in gradient estimation due to the correlation of estimates for image pixels and their derivatives and enables correct convergence of the optimizer even when using low sample counts in the renderer. To efficiently optimize our models in the presence of the Monte Carlo noise introduced by the BSSRDF integral, we introduce a dual-buffer method for evaluating the L2 image loss. Image gives more information such as colour, structure, etc., about the object. ![]() No light enters the eye from the shadow of the object. Image is seen when light coming from the object after reflection or refraction enters the observes’s eye. We use this differentiable rendering method in an end-to-end approach that jointly recovers heterogeneous translucent materials (represented by a BSSRDF) and detailed geometry of an object (represented by a mesh) from a sparse set of measured 2D images in a coarse-to-fine framework incorporating Laplacian preconditioning for the geometry. We present the first image-computable model that can predict human translucency judgments based on unsupervised learning from natural photographs of translucent objects. Shadow is formed when light falls on the opaque body. The light rays get scattered in the interior of such objects. Translucency (also called translucence or translucidity) allows light to pass through, but does not necessarily (again, on the macroscopic scale) follow Snells law the photons can be scattered at either of the two interfaces, or internally, where there is a change in index of refraction. This introduces new types of paths requiring new methods for sampling moving discontinuities in material space that arise from visibility and moving geometry. A substance is called translucent if it allows partial transmission. Representing translucency using a heterogeneous bidirectional scattering-surface reflectance distribution function (BSSRDF), we extend the framework of path-space differentiable rendering to accommodate both surface and subsurface reflection. Inverse rendering is a powerful approach to modeling objects from photographs, and we extend previous techniques to handle translucent materials that exhibit subsurface scattering. On real data, we show reconstruction of a slice of soap and cut cubes of kiwi and dragonfruit. ![]() For the synthetic data, we show jointly reconstructing the shape and subsurface scattering material of a bumpy object (first from left) a spatially varying extinction coefficient texture (left bunny) and a spatially varying single scattering reflectance texture (right bunny). Reconstruction results rendered in global illumination for both synthetic (left three objects) and real data (right three objects). Reconstructing translucent objects using differentiable rendering Hello, I am strugling to create a curtain material. ![]()
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