Live User-guided Intrinsic Video For Static Scenes

We present a novel real-time approach for user-guided intrinsic decomposition of static scenes captured by an RGB-D sensor. In the first step, we acquire a three-dimensional representation of the scene using a dense volumetric reconstruction framework. The obtained reconstruction serves as a proxy to densely fuse reflectance estimates and to store user-provided constraints in three-dimensional space. User constraints, in the form of constant shading and reflectance strokes, can be placed directly on the real-world geometry using an intuitive touch-based interaction metaphor, or using interactive mouse strokes. Fusing the decomposition results and constraints in three-dimensional space allows for robust propagation of this information to novel views by re-projection.We leverage this information to improve on the decomposition quality of existing intrinsic video decomposition techniques by further constraining the ill-posed decomposition problem. In addition to improved decomposition quality, we show a variety of live augmented reality applications such as recoloring of objects, relighting of scenes and editing of material appearance

Extending stochastic resonance for neuron models to general Levy noise

A recent paper by Patel and Kosko (2008) demonstrated stochastic resonance (SR) for general feedback continuous and spiking neuron models using additive Levy noise constrained to have finite second moments. In this brief, we drop this constraint and show that their result extends to general Levy noise models. We achieve this by showing that �¿large jump�¿ discontinuities in the noise can be controlled so as to allow the stochastic model to tend to a deterministic one as the noise dissipates to zero. SR then follows by a �¿forbidden intervals�¿ theorem as in Patel and Kosko's paper

Free-viewpoint Indoor Neural Relighting from Multi-view Stereo

We introduce a neural relighting algorithm for captured indoors scenes, that allows interactive free-viewpoint navigation. Our method allows illumination to be changed synthetically, while coherently rendering cast shadows and complex glossy materials. We start with multiple images of the scene and a 3D mesh obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is well-explained as the sum of a view-independent diffuse component and a view-dependent glossy term concentrated around the mirror reflection direction. We design a convolutional network around input feature maps that facilitate learning of an implicit representation of scene materials and illumination, enabling both relighting and free-viewpoint navigation. We generate these input maps by exploiting the best elements of both image-based and physically-based rendering. We sample the input views to estimate diffuse scene irradiance, and compute the new illumination caused by user-specified light sources using path tracing. To facilitate the network's understanding of materials and synthesize plausible glossy reflections, we reproject the views and compute mirror images. We train the network on a synthetic dataset where each scene is also reconstructed with MVS. We show results of our algorithm relighting real indoor scenes and performing free-viewpoint navigation with complex and realistic glossy reflections, which so far remained out of reach for view-synthesis techniques

Programmable Image-Based Light Capture for Previsualization

Previsualization is a class of techniques for creating approximate previews of a movie sequence in order to visualize a scene prior to shooting it on the set. Often these techniques are used to convey the artistic direction of the story in terms of cinematic elements, such as camera movement, angle, lighting, dialogue, and character motion. Essentially, a movie director uses previsualization (previs) to convey movie visuals as he sees them in his minds-eye . Traditional methods for previs include hand-drawn sketches, Storyboards, scaled models, and photographs, which are created by artists to convey how a scene or character might look or move. A recent trend has been to use 3D graphics applications such as video game engines to perform previs, which is called 3D previs. This type of previs is generally used prior to shooting a scene in order to choreograph camera or character movements. To visualize a scene while being recorded on-set, directors and cinematographers use a technique called On-set previs, which provides a real-time view with little to no processing. Other types of previs, such as Technical previs, emphasize accurately capturing scene properties but lack any interactive manipulation and are usually employed by visual effects crews and not for cinematographers or directors. This dissertation\u27s focus is on creating a new method for interactive visualization that will automatically capture the on-set lighting and provide interactive manipulation of cinematic elements to facilitate the movie maker\u27s artistic expression, validate cinematic choices, and provide guidance to production crews. Our method will overcome the drawbacks of the all previous previs methods by combining photorealistic rendering with accurately captured scene details, which is interactively displayed on a mobile capture and rendering platform. This dissertation describes a new hardware and software previs framework that enables interactive visualization of on-set post-production elements. A three-tiered framework, which is the main contribution of this dissertation is; 1) a novel programmable camera architecture that provides programmability to low-level features and a visual programming interface, 2) new algorithms that analyzes and decomposes the scene photometrically, and 3) a previs interface that leverages the previous to perform interactive rendering and manipulation of the photometric and computer generated elements. For this dissertation we implemented a programmable camera with a novel visual programming interface. We developed the photometric theory and implementation of our novel relighting technique called Symmetric lighting, which can be used to relight a scene with multiple illuminants with respect to color, intensity and location on our programmable camera. We analyzed the performance of Symmetric lighting on synthetic and real scenes to evaluate the benefits and limitations with respect to the reflectance composition of the scene and the number and color of lights within the scene. We found that, since our method is based on a Lambertian reflectance assumption, our method works well under this assumption but that scenes with high amounts of specular reflections can have higher errors in terms of relighting accuracy and additional steps are required to mitigate this limitation. Also, scenes which contain lights whose colors are a too similar can lead to degenerate cases in terms of relighting. Despite these limitations, an important contribution of our work is that Symmetric lighting can also be leveraged as a solution for performing multi-illuminant white balancing and light color estimation within a scene with multiple illuminants without limits on the color range or number of lights. We compared our method to other white balance methods and show that our method is superior when at least one of the light colors is known a priori