Romain Pacanowski

Light warping for enhanced surface depiction

Recent research on the human visual system shows that our perception of object shape relies in part on compression and stretching of the reflected lighting environment onto its surface. We use this property to enhance the shape depiction of 3D objects by locally warping the environment lighting around main surface features. Contrary to previous work, which require specific illumination, material characteristics and/or stylization choices, our approach enhances surface shape without impairing the desired appearance. Thanks to our novel local shape descriptor, salient surface features are explicitly extracted in a view-dependent fashion at various scales without the need of any pre-process. We demonstrate our system on a variety of rendering settings, using object materials ranging from diffuse to glossy, to mirror or refractive, with direct or global illumination, and providing styles that range from photorealistic to non-photorealistic. The warping itself is very fast to compute on modern graphics hardware, enabling real-time performance in direct illumination scenarios. Note: Third-Party Material Attribution Third-party material used in ACM Transactions on Graphics 28(3), Article 25 - Light Warping for Enhanced Surface Depiction, by Vergne, Pacanowski, Barla, Granier, and Schlick - was used without proper attribution. The 3D model used in Figures 1, 3, and 5, as well as in the cover image of this volume of the journal, was downloaded from the Shape Repository of AIM@SHAPE Project (http://shapes.aimatshape.net) and is the property of CNR-IMATI. We regret this oversight.

Radiance Scaling for versatile surface enhancement

We present a novel technique called Radiance Scaling for the depiction of surface shape through shading. It adjusts reflected light intensities in a way dependent on both surface curvature and material characteristics. As a result, diffuse shading or highlight variations become correlated to surface feature variations, enhancing surface concavities and convexities. This approach is more versatile compared to previous methods. First, it produces satisfying results with any kind of material: we demonstrate results obtained with Phong and Ashikmin BRDFs, Cartoon shading, sub-Lambertian materials, and perfectly reflective or refractive objects. Second, it imposes no restriction on lighting environment: it does not require a dense sampling of lighting directions and works even with a single light. Third, it makes it possible to enhance surface shape through the use of precomputed radiance data such as Ambient Occlusion, Prefiltered Environment Maps or Lit Spheres. Our novel approach works in real-time on modern graphics hardware.

Optimizing BRDF orientations for the manipulation of anisotropic highlights

This paper introduces a system for the direct editing of highlights produced by anisotropic BRDFs, which we call anisotropic highlights. We first provide a comprehensive analysis of the link between the direction of anisotropy and the shape of highlight curves for arbitrary object surfaces. The gained insights provide the required ingredients to infer BRDF orientations from a prescribed highlight tangent field. This amounts to a non‐linear optimization problem, which is solved at interactive framerates during manipulation. Taking inspiration from sculpting software, we provide tools that give the impression of manipulating highlight curves while actually modifying their tangents. Our solver produces desired highlight shapes for a host of lighting environments and anisotropic BRDFs.

ALTA: a BRDF analysis library

In this document, we introduce ALTA, a cross platform generic open-source library for Bidirectional Reflectance Distribution Function (BRDF) analysis. Among others, ALTA permits to estimate BRDF models parameters from measured data, to perform statistical analysis and also to export BRDF data models in a wide variety of formats.

Position-Dependent Importance Sampling of Light Field Luminaires

The possibility to use real world light sources (aka luminaires) for synthesizing images greatly contributes to their physical realism. Among existing models, the ones based on light fields are attractive due to their ability to represent faithfully the near-field and due to their possibility of being directly acquired. In this paper, we introduce a dynamic sampling strategy for complex light field luminaires with the corresponding unbiased estimator. The sampling strategy is adapted, for each 3D scene position and each frame, by restricting the sampling domain dynamically and by balancing the number of samples between the different components of the representation. This is achieved efficiently by simple position-dependent affine transformations and restrictions of Cumulative Distributive Functions that ensure that every generated sample conveys energy and contributes to the final result. Therefore, our approach only requires a low number of samples to achieve almost converged results. We demonstrate the efficiency of our approach on modern hardware by introducing a GPU-based implementation. Combined with a fast shadow algorithm, our solution exhibits interactive frame rates for direct lighting for large measured luminaires.

Cache-friendly micro-jittered sampling

Monte-Carlo integration techniques for global illumination are popular on GPUs thanks to their massive parallel architecture, but efficient implementation remains challenging. The use of randomly decorrelated low-discrepancy sequences in the path-tracing algorithm allows faster visual convergence. However, the parallel tracing of incoherent rays often results in poor memory cache utilization, reducing the ray bandwidth efficiency. Interleaved sampling [Keller et al. 2001] partially solves this problem, by using a small set of distributions split in coherent ray-tracing passes, but the solution is prone to structured noise. On the other hand, ray-reordering methods [Pharr et al. 1997] group stochastic rays into coherent ray packets but their implementation add an additional sorting cost on the GPU [Moon et al. 2010] [Garanzha and Loop 2010].