Blog

: We present a method for capturing the BSSRDF (bidirectional scattering-surface reflectance distribution function) of arbitrary geometry with a neural network. We demonstrate how a compact neural network can represent the full 8-dimensional light transport within an object including heterogeneous scattering. We develop an efficient rendering method using importance sampling that is able to render complex translucent objects under arbitrary lighting. Our method can also leverage the common planar half-space assumption, which allows it to represent one BSSRDF model that can be used across a variety of geometries. Our results demonstrate that we can render heterogeneous translucent objects under arbitrary lighting and obtain results that match the reference rendered using volumetric path tracing.

: We present a practical global error estimation technique for Monte Carlo ray tracing combined with deep learning based denoising. Our method uses aggregated estimates of bias and variance to determine the squared error distribution of the pixels. Unlike unbiased estimates for classical Monte Carlo ray tracing, this distribution follows a noncentral chi-squared distribution, under reasonable assumptions. Based on this, we develop a stopping criterion for denoised Monte Carlo image synthesis that terminates rendering once a user specified error threshold has been achieved. Our results demonstrate that our error estimate and stopping criterion work well on a variety of scenes, and that we are able to achieve a given error threshold without the user specifying the number of samples needed.

: Monte Carlo rendering of translucent objects with heterogeneous scattering properties is often expensive both in terms of memory and computation. If the scattering properties are described by a 3D texture, memory consumption is high. If we do path tracing and use a high dynamic range lighting environment, the computational cost of the rendering can easily become significant. We propose a compact and efficient neural method for representing and rendering the appearance of heterogeneous translucent objects. Instead of assuming only surface variation of optical properties, our method represents the appearance of a full object taking its geometry and volumetric heterogeneities into account. This is similar to a neural radiance field, but our representation works for an arbitrary distant lighting environment. In a sense, we present a version of neural precomputed radiance transfer that captures relighting of heterogeneous translucent objects. We use a multi-layer perceptron (MLP) with skip connections to represent the appearance of an object as a function of spatial position, direction of observation, and direction of incidence. The latter is considered a directional light incident across the entire non-self-shadowed part of the object. We demonstrate the ability of our method to compactly store highly complex materials while having high accuracy when comparing to reference images of the represented object in unseen lighting environments. As compared with path tracing of a heterogeneous light scattering volume behind a refractive interface, our method more easily enables importance sampling of the directions of incidence and can be integrated into existing rendering frameworks while achieving interactive frame rates.

: Inverse rendering has emerged as a standard tool to reconstruct the parameters of appearance models from images (e.g., textured BSDFs). In this work, we present several novel contributions motivated by the practical challenges of recovering high-resolution surface appearance textures, including spatially-varying subsurface scattering parameters.
First, we propose Laplacian mipmapping, which combines differentiable mipmapping and a Laplacian pyramid representation into an effective preconditioner. This seemingly simple technique significantly improves the quality of recovered surface textures on a set of challenging inverse rendering problems. Our method automatically adapts to the render and texture resolutions, only incurs moderate computational cost and achieves better quality than prior work while using fewer hyperparameters. Second, we introduce a specialized gradient computation algorithm for textured, path-traced subsurface scattering, which facilitates faithful reconstruction of translucent materials. By using path tracing, we enable the recovery of complex appearance while avoiding the approximations of the previously used diffusion dipole methods. Third, we demonstrate the application of both these techniques to reconstructing the textured appearance of human faces from sparse captures. Our method recovers high-quality relightable appearance parameters that are compatible with current production renderers.

In the domain of wood modeling, we present a new complex appearance model, coupled with a user-friendly NLP-based frontend for intuitive interactivity. First, we present a procedurally generated wood model that is capable of accurately simulating intricate wood characteristics, including growth rings, vessels/pores, rays, knots, and figure. Furthermore, newly developed features were introduced, including brushiness distortion, influence points, and individual feature control. These novel enhancements facilitate a more precise matching between procedurally generated wood and ground truth images. Second, we present a text-based user interface that relies on a trained natural language processing model that is designed to map user plain English requests into the parameter space of our procedurally generated wood model. This significantly reduces the complexity of the authoring process, thereby enabling any user, regardless of their level of woodworking expertise or familiarity with procedurally generated materials, to utilize it to its fullest potential.

Conference or journal: SIGGRAPH 2024 Date : August 2024 Philippe Weier, Alexander Rath, Élie Michel, Iliyan Georgiev, Philipp Slusallek and Tamy BoubekeurNeural representations have shown spectacular ability to compress complex signals in a fraction of the raw data size. In …

Conference or journal: Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2024 Date : 2024.06 Andrei-Timotei Ardelean, Tim WeyrichAnomaly detection and localization in images is a growing field in computer vision. In this area, a seemingly understudied problem is …

Conference or journal: Winter Conference on Applications of Computer Vision 2024 Date : 2024.01 Andrei-Timotei Ardelean, Tim WeyrichWe propose a novel method for Zero-Shot Anomaly Localization on textures. The task refers to identifying abnormal regions in an otherwise homogeneous image. …

Conference or journal: Computer Graphics Forum (Eurographics 2024) Date : 2024.04 Dario Lanza, Adrian Jarabo, Belen MasiaThis work presents a perceptually-motivated manifold for translucent appearance, designed for intuitive editing of translucent materials by navigating through the manifold. Classic tools for …

Conference or journal: Computer Graphics Forum (EGSR 2024) Date : 2024.07 Dario Lanza, Juan Raúl Padrón-Griffe, Alina Pranovich, Adolfo Muñoz, Jeppe Frisvad, Adrian JaraboWe present an appearance model for cosmetics, in particular for foundation layers, that reproduces a range of …