Abstract: Inverse rendering is important for training neural networks for generative artificial intelligence (AI), and it involves inverting the rendering process by taking an image and converting it into scene or model parameters that can be backpropagated through a network, helping to train the network to learn to generate models, materials, textures, etc. Because of the gradients required for this backpropagation, inverse rendering requires differentiable rendering algorithms. Current differentiable renderers are based on rasterization which make it difficult to learn scene properties depending on second order effects. The present disclosure provides closest silhouette queries for computing differential visibility, which can be used for inverse rendering.

Abstract: High quality image rendering can be achieved in part by using inverse transform sampling to direct sampling toward regions of greater importance, such as regions with higher brightness values, to reduce noise and improve convergence. Inverse transform sampling can be achieved more efficiently by reformulating as a ray-tracing problem, using tree traversal units that can be accelerated. A geometric mesh can be generated based on a set of cumulative distribution functions (CDFs) for various rows and columns of pixels in a texture, and individual rays can be traced against this mesh, with those rays having a higher probability of intersection at a point with greater importance, such as a higher brightness value. A probability distribution function to be used for importance sampling can be derived by analyzing partial derivatives of the CDF geometry at the intersection location.

Abstract: High quality image rendering can be achieved in part by using inverse transform sampling to direct sampling toward regions of greater importance, such as regions with higher brightness values, to reduce noise and improve convergence. Inverse transform sampling can be achieved more efficiently by reformulating as a ray-tracing problem, using tree traversal units that can be accelerated. A geometric mesh can be generated based on a set of cumulative distribution functions (CDFs) for various rows and columns of pixels in a texture, and individual rays can be traced against this mesh, with those rays having a higher probability of intersection at a point with greater importance, such as a higher brightness value. A probability distribution function to be used for importance sampling can be derived by analyzing partial derivatives of the CDF geometry at the intersection location.

Abstract: High quality image rendering can be achieved in part by using inverse transform sampling to direct sampling toward regions of greater importance, such as regions with higher brightness values, to reduce noise and improve convergence. Inverse transform sampling can be achieved more efficiently by reformulating as a ray-tracing problem, using tree traversal units that can be accelerated. A geometric mesh can be generated based on a set of cumulative distribution functions (CDFs) for various rows and columns of pixels in a texture, and individual rays can be traced against this mesh, with those rays having a higher probability of intersection at a point with greater importance, such as a higher brightness value. A probability distribution function to be used for importance sampling can be derived by analyzing partial derivatives of the CDF geometry at the intersection location.

Abstract: High quality image rendering can be achieved in part by using inverse transform sampling to direct sampling toward regions of greater importance, such as regions with higher brightness values, to reduce noise and improve convergence. Inverse transform sampling can be achieved more efficiently by reformulating as a ray-tracing problem, using tree traversal units that can be accelerated. A geometric mesh can be generated based on a set of cumulative distribution functions (CDFs) for various rows and columns of pixels in a texture, and individual rays can be traced against this mesh, with those rays having a higher probability of intersection at a point with greater importance, such as a higher brightness value. A probability distribution function to be used for importance sampling can be derived by analyzing partial derivatives of the CDF geometry at the intersection location.

Abstract: High quality image rendering can be achieved in part by using inverse transform sampling to direct sampling toward regions of greater importance, such as regions with higher brightness values, to reduce noise and improve convergence. Inverse transform sampling can be achieved more efficiently by reformulating as a ray-tracing problem, using tree traversal units that can be accelerated. A geometric mesh can be generated based on a set of cumulative distribution functions (CDFs) for various rows and columns of pixels in a texture, and individual rays can be traced against this mesh, with those rays having a higher probability of intersection at a point with greater importance, such as a higher brightness value. A probability distribution function to be used for importance sampling can be derived by analyzing partial derivatives of the CDF geometry at the intersection location.