Local Light Alignment for Multi-Scale Shape Depiction

International audienceMotivated by recent findings in the field of visual perception, we present a novel approach for enhancing shape depiction and perception of surface details. We propose a shading-based technique that relies on locally adjusting the direction of light to account for the different components of materials. Our approach ensures congruence between shape and shading flows, leading to an effective enhancement of the perception of shape and details while impairing neither the lighting nor the appearance of materials. It is formulated in a general way allowing its use for multiple scales enhancement in real-time on the GPU, as well as in global illumination contexts. We also provide artists with fine control over the enhancement at each scale

Optimization techniques for computationally expensive rendering algorithms

Realistic rendering in computer graphics simulates the interactions of light and surfaces. While many accurate models for surface reflection and lighting, including solid surfaces and participating media have been described; most of them rely on intensive computation. Common practices such as adding constraints and assumptions can increase performance. However, they may compromise the quality of the resulting images or the variety of phenomena that can be accurately represented. In this thesis, we will focus on rendering methods that require high amounts of computational resources. Our intention is to consider several conceptually different approaches capable of reducing these requirements with only limited implications in the quality of the results. The first part of this work will study rendering of time-­¿varying participating media. Examples of this type of matter are smoke, optically thick gases and any material that, unlike the vacuum, scatters and absorbs the light that travels through it. We will focus on a subset of algorithms that approximate realistic illumination using images of real world scenes. Starting from the traditional ray marching algorithm, we will suggest and implement different optimizations that will allow performing the computation at interactive frame rates. This thesis will also analyze two different aspects of the generation of anti-­¿aliased images. One targeted to the rendering of screen-­¿space anti-­¿aliased images and the reduction of the artifacts generated in rasterized lines and edges. We expect to describe an implementation that, working as a post process, it is efficient enough to be added to existing rendering pipelines with reduced performance impact. A third method will take advantage of the limitations of the human visual system (HVS) to reduce the resources required to render temporally antialiased images. While film and digital cameras naturally produce motion blur, rendering pipelines need to explicitly simulate it. This process is known to be one of the most important burdens for every rendering pipeline. Motivated by this, we plan to run a series of psychophysical experiments targeted at identifying groups of motion-­¿blurred images that are perceptually equivalent. A possible outcome is the proposal of criteria that may lead to reductions of the rendering budgets

Cross-dimensional Analysis for Improved Scene Understanding

Visual data have taken up an increasingly large role in our society. Most people have instant access to a high quality camera in their pockets, and we are taking more pictures than ever before. Meanwhile, through the advent of better software and hardware, the prevalence of 3D data is also rapidly expanding, and demand for data and analysis methods is burgeoning in a wide range of industries. The amount of information about the world implicitly contained in this stream of data is staggering. However, as these images and models are created in uncontrolled circumstances, the extraction of any structured information from the unstructured pixels and vertices is highly non-trivial. To aid this process, we note that the 2D and 3D data modalities are similar in content, but intrinsically different in form. Exploiting their complementary nature, we can investigate certain problems in a cross-dimensional fashion - for example, where 2D lacks expressiveness, 3D can supplement it; where 3D lacks quality, 2D can provide it. In this thesis, we explore three analysis tasks with this insight as our point of departure. First, we show that by considering the tasks of 2D and 3D retrieval jointly we can improve performance of 3D retrieval while simultaneously enabling interesting new ways of exploring 2D retrieval results. Second, we discuss a compact representation of indoor scenes called a "scene map", which represents the objects in a scene using a top-down map of object locations. We propose a method for automatically extracting such scene maps from single 2D images using a database of 3D models for training. Finally, we seek to convert single 2D images to full 3D scenes using a database of 3D models as input. Occlusion is handled by modelling object context explicitly, allowing us to identify and pose objects that would otherwise be too occluded to make inferences about. For all three tasks, we show the utility of our cross-dimensional insight by evaluating each method extensively and showing favourable performance over baseline methods

Measuring the Perception of Light Inconsistencies

In this paper we explore the ability of the human visual system to detect inconsistencies in the illumination of objects in images. We specifically focus on objects being lit from different angles as the rest of the image. We present the results of three different tests, two with synthetic objects and a third one with digitally manipulated real images. Our results seem to agree with previous publications exploring the topic, but we extend them by providing quantifiable data which in turn suggest approximate perceptual thresholds. Given that light detection in single images is an ill-posed problem, these thresholds can provide valid error limits to related algorithms in different contexts, such as compositing or augmented reality