Abstract: A scene modeling system accesses a three-dimensional (3D) scene including a 3D object. The scene modeling system applies a silhouette bidirectional texture function (SBTF) model to the 3D object to generate an output image of a textured material rendered as a surface of the 3D object. Applying the SBTF model includes determining a bounding geometry for the surface of the 3D object. Applying the SBTF model includes determining, for each pixel of the output image, a pixel value based on the bounding geometry. The scene modeling system displays, via a user interface, the output image based on the determined pixel values.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate three-dimensional hybrid mesh-volumetric representations for digital objects. For instance, in one or more embodiments, the disclosed systems generate a mesh for a digital object from a plurality of digital images that portray the digital object using a multi-view stereo model. Additionally, the disclosed systems determine a set of sample points for a thin volume around the mesh. Using a neural network, the disclosed systems further generate a three-dimensional hybrid mesh-volumetric representation for the digital object utilizing the set of sample points for the thin volume and the mesh.

Abstract: An apparatus in one embodiment comprises a multi-stage processing pipeline configured to generate a procedural yarn model by fitting procedural yarn model parameters to input data comprising computed tomography measurements of one or more actual yarn samples. The apparatus further comprises an image rendering system configured to execute one or more procedural yarn generation algorithms utilizing the procedural yarn model and to generate based at least in part on results of execution of the one or more procedural yarn generation algorithms at least one corresponding output image for presentation on a display. The multi-stage processing pipeline comprises a first stage configured to perform ply twisting estimation and ply cross-sectional estimation, a second stage configured to classify constituent fibers into regular fibers and flyaway fibers, and third and fourth stages configured to process the respective regular and flyaway fibers to fit respective different sets of parameters of the procedural yarn model.

Abstract: An apparatus in one embodiment comprises a multi-stage processing pipeline configured to generate a procedural yarn model by fitting procedural yarn model parameters to input data comprising computed tomography measurements of one or more actual yarn samples. The apparatus further comprises an image rendering system configured to execute one or more procedural yarn generation algorithms utilizing the procedural yarn model and to generate based at least in part on results of execution of the one or more procedural yarn generation algorithms at least one corresponding output image for presentation on a display. The multi-stage processing pipeline comprises a first stage configured to perform ply twisting estimation and ply cross-sectional estimation, a second stage configured to classify constituent fibers into regular fibers and flyaway fibers, and third and fourth stages configured to process the respective regular and flyaway fibers to fit respective different sets of parameters of the procedural yarn model.