Frédéric Mora

Lazy Visibility Evaluation for Exact Soft Shadows

This paper presents a novel approach to compute high quality and noise‐free soft shadows using exact visibility computations. This work relies on a theoretical framework allowing to group lines according to the geometry they intersect. From this study, we derive a new algorithm encoding lazily the visibility from a polygon. Contrary to previous works on from‐polygon visibility, our approach is very robust and straightforward to implement. We apply this algorithm to solve exactly and efficiently the visibility of an area light source from any point in a scene. As a consequence, results are not sensitive to noise, contrary to soft shadows methods based on area light source sampling. We demonstrate the reliability of our approach on different scenes and configurations.

Technical Section: Analytic ambient occlusion using exact from-polygon visibility

This paper presents a new method to compute exact from-polygon visibility, as well as a possible application to the calculation of high quality ambient occlusion. The starting point of this work is a theoretical framework which allows to group lines together according to the geometry they intersect. By applying this study in the context of from-polygon visibility, we derive an analytical definition which permits us to group the view rays according to the first geometry they encounter. Our new algorithm encodes lazily the visibility from a polygon. In contrast to the previous works on from-polygon visibility, our approach is very robust. For each point in the scene, the algorithm efficiently calculates the exact fragments of visible geometry. By combining this information with a analytical definition of ambient occlusion, we achieve results that are not sensitive to noise or other visual imperfections, contrary to ambient occlusion methods which are based either on sampling or visibility approximations.

A Framework for n -Dimensional Visibility Computations

This chapter introduces global visibility computation using Grassmann Algebra. Visibility computation is a fundamental task in computer graphics, as in many other scientific domains. While it is well understood in two dimensions, this does not remain true in higher-dimensional spaces.

Partitioned Shadow Volumes

Real‐time shadows remain a challenging problem in computer graphics. In this context, shadow algorithms generally rely either on shadow mapping or shadow volumes. This paper rediscovers an old class of algorithms that build a binary space partition over the shadow volumes. For almost 20 years, such methods have received little attention as they have been considered lacking of both robustness and efficiency. We show that these issues can be overcome, leading to a simple and robust shadow algorithm. Hence we demonstrate that this kind of approach can reach a high level of performance. Our algorithm uses a new partitioning strategy which avoids any polygon clipping. It relies on a Ternary Object Partitioning tree, a new data structure used to find if an image point is shadowed. Our method works on a triangle soup and its memory footprint is fixed. Our experiments show that it is efficient and robust, including for finely tessellated models.

Photon mapping with visible kernel domains

Despite the strong efforts made in the last three decades, lighting simulation systems still remain prone to various types of imprecisions. This paper specifically tackles the problem of biases due to density estimation used in photon mapping approaches. We study the fundamental aspects of density estimation and exhibit the need for handling visibility in the early stage of the kernel domain definition. We show that properly managing visibility in the density estimation process allows to reduce or to remove biases all at once. In practice, we have implemented a 3D product kernel based on a polyhedral domain, with both point-to-point and point-to-surface visibility computation. Our experimental results illustrate the enhancements produced at every stage of density estimation, for direct photon maps visualization and progressive photon mapping.

Deep partitioned shadow volumes using stackless and hybrid traversals

Computing accurate hard shadows is a difficult problem in interactive rendering. Previous methods rely either on Shadow Maps or Shadow Volumes. Recently Partitioned Shadow Volumes (PSV) has been introduced. It revisits the old Shadow Volumes Binary Tree Space Partitioning algorithm, leading to a practicable and efficient technique. In this article, we analyze the PSV query algorithm and identify two main drawbacks: First, it uses a stack which is not GPU friendly; its size must be small enough to reduce the register pressure, but large enough to avoid stack overflow. Second, PSV struggles with configurations involving significant depth complexity, especially for lit points. We solve these problems by adding a depth information to the PSV data structure, and by designing a stackless query. In addition, we show how to combine the former PSV query with our stackless solution, leading to a hybrid technique taking advantage of both. This eliminates any risk of stack overflow, and our experiments demonstrate that these improvements accelerate the rendering time up to a factor of 3.