Concrete Computation of Global Illumination Using Structured Sampling

A new methodology is presented for the computation of global illumination using structured sampling. Analytical/numerical solutions for illumination are developed for simple lighting configurations. These solutions are subsequently used to generate accurate reference images. The structured sampling solution for global illumination is then discussed, comprising sample placement for illumination calculation, reconstruction for light transfer and finally resampling and filtering of illumination samples for display. A first approximation to this technique is presented using a priori placement of samples, irregular polygon reflectors, grid resampling and a conical filter for display. The new algorithm is evaluated for image quality, and compared to the traditional radiosity-based approach. These first results show that the structured sampling solution yields significant computational savings while maintaining high image quality. 1. Goals of the Approach The calculation of global illumination is inherently complex, even for environments that are simpl

Improving NeRF Quality by Progressive Camera Placement for Unrestricted Navigation in Complex Environments

Neural Radiance Fields, or NeRFs, have drastically improved novel view synthesis and 3D reconstruction for rendering. NeRFs achieve impressive results on object-centric reconstructions, but the quality of novel view synthesis with free-viewpoint navigation in complex environments (rooms, houses, etc) is often problematic. While algorithmic improvements play an important role in the resulting quality of novel view synthesis, in this work, we show that because optimizing a NeRF is inherently a data-driven process, good quality data play a fundamental role in the final quality of the reconstruction. As a consequence, it is critical to choose the data samples -- in this case the cameras -- in a way that will eventually allow the optimization to converge to a solution that allows free-viewpoint navigation with good quality. Our main contribution is an algorithm that efficiently proposes new camera placements that improve visual quality with minimal assumptions. Our solution can be used with any NeRF model and outperforms baselines and similar work

Can VR be Useful and Usable in Real-World Contexts? Observations from the Application and Evaluation of VR in Realistic Usage Conditions

International audienceThis paper presents our observations from the use of high-end projection-based VR in different real-world settings, with practitioners but also novice users that do not normally use VR in their everyday practice. We developed two applications for two different content domains and present case studies of actual experiences with professionals and students who used these as part of their work or during their museum visit. Emphasis is given on usability issues and evaluation of effectiveness, as well as on our thoughts on the efficacy of the long term deployment of VR under realistic usage conditions, especially when the technology becomes mundane and the content takes precedence over the display medium. We will present an overall assessment of our experience, on issues relating to usability and user satisfaction with VR in real-world contexts

Auditory-visual virtual reality as a diagnostic and therapeutic tool for cynophobia

International audienceTraditionally, virtual reality exposure-based treatment concentrates primarily on the presentation of a high fidelity visual experience. However, adequately combining the visual and the auditory experience provides a powerful tool to enhance sensory processing and modulate attention. We present the design and usability testing of an auditory-visual interactive environment for investigating virtual reality exposurebased treatment for cynophobia. The specificity of our application is that it involves 3D sound, allowing the presentations and spatial manipulation of a fearful stimulus in the auditory modality, in the visual modality, and both. We conducted an evaluation test with 10 dog fearful participants in order to assess the capacity of our auditory-visual virtual environment to generate fear reactions. The specific perceptual characteristics of the dog model that were implemented in the virtual environment were highly arousing, suggesting that virtual reality is a promising tool to treat cynophobia

Accurate, Consistent Reconstruction of Illumination Functions Using Structured Sampling

International audienceThe study of common classes of diffuse emitters, such as planar convex polygons, reveals several interesting properties of the functions of illumination these emitters cast on receiver surfaces. Some properties, such as the position of the maximum and the curvature are of particular interest for sampling and reconstruction of illumination across receivers. A computationally efficient approach is presented that identifies these properties, and uses them to select samples of illumination. In addition these properties are used to determine upper bounds on the error due to linear and quadratic interpolants. These bounds are then used to adaptively subdivide the nonuniform sampling grid, resulting in accurate reconstruction. Results show that the method reduces the error compared to uniform approaches, and produces more consistent animated sequences

View Dependent Layered Projective Texture Maps

International audienceCapturing and rendering of real scenes in an immersive virtual environment is still a challenging task. We present a novel workflow, using high-quality, view-dependent projective texturing at low texture memory cost, while reinforcing artist's control over image quality. Photographs of a real scene are first used to create a 3D model with standard tools. Our method automatically orders geometry into optimized visibility layers for each photograph. These layers are subsequently used to create standard 2D image-editing layers, enabling artists to fill in missing texture using standard techniques such as clone brushing. The result of this preprocess is used by our novel layered projective texture rendering algorithm, which has low texture memory consumption, high interactive image quality and avoids the need for subdividing geometry for visibility. We show results of our implementation on a real-world project

Flexible SVBRDF Capture with a Multi-Image Deep Network

International audienceEmpowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures required by traditional optimization-based approaches. However, a single image is often simply not enough to observe the rich appearance of real-world materials. We present a deep-learning method capable of estimating material appearance from a variable number of uncalibrated and unordered pictures captured with a handheld camera and flash. Thanks to an order-independent fusing layer, this architecture extracts the most useful information from each picture, while benefiting from strong priors learned from data. The method can handle both view and light direction variation without calibration. We show how our method improves its prediction with the number of input pictures, and reaches high quality reconstructions with as little as 1 to 10 images-a sweet spot between existing single-image and complex multi-image approaches