Mathias Paulin

Implicit skinning: real-time skin deformation with contact modeling

Geometric skinning techniques, such as smooth blending or dual-quaternions, are very popular in the industry for their high performances, but fail to mimic realistic deformations. Other methods make use of physical simulation or control volume to better capture the skin behavior, yet they cannot deliver real-time feedback. In this paper, we present the first purely geometric method handling skin contact effects and muscular bulges in real-time. The insight is to exploit the advanced composition mechanism of volumetric, implicit representations for correcting the results of geometric skinning techniques. The mesh is first approximated by a set of implicit surfaces. At each animation step, these surfaces are combined in real-time and used to adjust the position of mesh vertices, starting from their smooth skinning position. This deformation step is done without any loss of detail and seamlessly handles contacts between skin parts. As it acts as a post-process, our method fits well into the standard animation pipeline. Moreover, it requires no intensive computation step such as collision detection, and therefore provides real-time performances.

A gradient-based implicit blend

We introduce a new family of binary composition operators that solves four major problems of constructive implicit modeling: suppressing bulges when two shapes merge, avoiding unwanted blending at a distance, ensuring that the resulting shape keeps the topology of the union, and enabling sharp details to be added without being blown up. The key idea is that field functions should not only be combined based on their values, but also on their gradients. We implement this idea through a family of C∞ composition operators evaluated on the GPU for efficiency, and illustrate it by applications to constructive modeling and animation.

Combinatorial Bidirectional Path-Tracing for Efficient Hybrid CPU/GPU Rendering

This paper presents a reformulation of bidirectional path‐tracing that adequately divides the algorithm into processes efficiently executed in parallel on both the CPU and the GPU. We thus benefit from high‐level optimization techniques such as double buffering, batch processing, and asyncronous execution, as well as from the exploitation of most of the CPU, GPU, and memory bus capabilities. Our approach, while avoiding pure GPU implementation limitations (such as limited complexity of shaders, light or camera models, and processed scene data sets), is more than ten times faster than standard bidirectional path‐tracing implementations, leading to performance suitable for production‐oriented rendering engines.

Accurate Shadows by Depth Complexity Sampling

The accurate generation of soft shadows is a particularly computationally intensive task. In order to reduce rendering time, most real‐time and offline applications decorrelate the generation of shadows from the computation of lighting. In addition to such approximations, they generate shadows using some restrictive assumptions only correct in very specific cases, leading to penumbra over‐estimation or light‐leaking artifacts. In this paper we present an algorithm that produces soft shadows without exhibiting the previous drawbacks. Using a new efficient evaluation of the number of occluders between two points (i.e. the depth complexity) we either modulate direct lighting or numerically solve the rendering equation for direct illumination. Our approach approximates shadows cast by semi‐opaque occluders and naturally handles area lights with spatially varying luminance. Furthermore, depending on the desired performance and quality, the resulting shadows are either very close to, or as accurate as, a ray‐traced reference. As a result, the presented method is well suited to many domains, ranging from quality‐sensitive to performance‐critical applications.

BRDF Measurement Modelling using Wavelets for Efficient Path Tracing

Physically based rendering needs numerical models from real measurements, or analytical models from material definitions, of the Bidirectional Reflectance Distribution Function (BRDF). However, measured BRDF data sets are too large and provide no functionalities to be practically used in Monte Carlo path tracing algorithms. In this paper, we present a wavelet‐based generic BRDF model suitable for both physical analysis and path tracing. The model is based on the separation of spectral and geometrical aspect of the BRDF and allows a compact and efficient representation of isotropic, anisotropic and/or spectral BRDFs. After a brief survey of BRDF and wavelet theory, we present our software architecture for generic wavelet transform and how to use it to model BRDFs. Then, modelling results are presented on real and virtual BRDF measurements. Finally, we show how to exploit the multiresolution property of the wavelet encoding to reduce the variance by importance sampling in a path tracing algorithm.

Real-Time Hierarchical Binary-Scene Voxelization

Volumetric representations provide the localization of shapes in space. When such representation is created on the fly from the geometry, it becomes very useful for a wide range of applications (constructive solid geometry (CSG), shape repair, collision detection, etc. Using the advanced functionalities provided by recent GPUs (geometry shaders, 32-bit integer texture format and bitwise operators), we show how to compute a robust scene voxelization and octree construction in a few milliseconds from any hardware-supported rasterizable geometry. Our hierarchical volumetric representation is thus especially well-suited for hierarchical computation of fully dynamic scenes.

An Extended Radiosity Using Parallel Ray-Traced Specular Transfers

The realism in image synthesis needs complex illumination models. In this paper, our goal is to describe a parallel extended radiosity method with general reflectance functions. This approach will allow us to produce realistic images. At first, we analyse existing extended radiosity methods to explain the energy transfer principles and how to compute them. Then we study theoretical frameworks on radiance and luminance transfers in a close environment to deduce a progressive extended radiosity method with parallel ray-traced specular transfers. Then we describe our implementation of this method in the VOXAR machine, parallel architecture dedicated to the ray-tracing algorithm.

Hybrid CPU/GPU KD-Tree Construction for Versatile Ray Tracing

In this paper, we propose an hybrid CPU-GPU ray-tracing implementation based on an optimal Kd-Tree as acceleration structure. The construction and traversal of this KD-tree takes benefits from both the CPU and the GPU to achieve high-performance ray-tracing on mainstream hardware. Our approach, flexible enough to use only one computing units (CPU or GPU), is able to efficiently distribute workload between CPUs and GPUs for fast construction and traversal of the KD-tree.

Representativity for Robust and Adaptive Multiple Importance Sampling

We present a general method enhancing the robustness of estimators based on multiple importance sampling (MIS) in a numerical integration context. MIS minimizes variance of estimators for a given sampling configuration, but when this configuration is less adapted to the integrand, the resulting estimator suffers from extra variance. We address this issue by introducing the notion of representativity” of a sampling strategy, and demonstrate how it can be used to increase robustness of estimators, by adapting them to the integrand. We first show how to compute representativities using common rendering informations such as BSDF, photon maps, or caches in order to choose the best sampling strategy for MIS. We then give hints to generalize our method to any integration problem and demonstrate that it can be used successfully to enhance robustness in different common rendering algorithms.

Spectral BRDF modeling using wavelets

The Bi-directional Reflectance Distribution Function (BRDF) is a complex function characterizing the reflection of light on a surface. It depends on five variables: four angles (lighting direction and observer direction) and the wavelength. A complete measurement campaign generates a large data set difficult to model. One way to proceed is to fit an analytical model on this data set. A numerical optimization technique, like simplex, allows to retrieve the best parameters of the model by minimizing the error with regard to measurements. Most of the analytical models obtain poor results for specular surfaces, and no wavelength dependent model actually exists. These reasons lead us to choose a numerical approach and particularly wavelets. This paper shows how wavelets can be used to provide an efficient BRDF model. Results of modeling are presented over a large collection of measurement data sets. At fixed wavelength, wavelet model has pretty good results, comparable to the best analytical models for diffuse surfaces, and much better for specular surfaces. The global relative error is lower than 5% with a compression ratio better than 90%. For spectral data sets, the wavelet model also presents very interesting performances with compression ratios greater than 95% and error lower than 2%.