Enseñanza de la capacidad eléctrica por analogía con un cilindro de gas natural comprimido

Introducimos una analogía que intenta ayudar en la enseñanza del concepto de capacidad eléctrica. La misma está destinada, principalmente, a alumnos y alumnas de nivel medio y del ciclo básico universitario con dificultades en el aprendizaje de conceptos abstractos como éste, y se basa en la comparación del capacitor con un recipiente de gas natural comprimido (GNC) como el que utilizan muchos vehículos en la actualidad. Este modelo puede facilitar la comprensión de la relación entre la carga eléctrica adquirida por un capacitor y la diferencia de potencial aplicada entre sus placas. Por otra parte, el contexto gaseoso, más familiar para la mayoría del alumnado, ayuda a comprender muchas situaciones en las que participan dos capacitores, las que tradicionalmente presentan dificultades para el aprendizaje

High-Dynamic-Range Lighting Estimation From Face Portraits.

We present a CNN-based method for outdoor highdynamic-range (HDR) environment map prediction from low-dynamic-range (LDR) portrait images. Our method relies on two different CNN architectures, one for light encoding and another for face-to-light prediction. Outdoor lighting is characterised by an extremely high dynamic range, and thus our encoding splits the environment map data between low and high-intensity components, and encodes them using tailored representations. The combination of both network architectures constitutes an end-to-end method for accurate HDR light prediction from faces at real-time rates, inaccessible for previous methods which focused on low dynamic range lighting or relied on non-linear optimisation schemes. We train our networks using both real and synthetic images, we compare our light encoding with other methods for light representation, and we analyse our results for light prediction on real images. We show that our predicted HDR environment maps can be used as accurate illumination sources for scene renderings, with potential applications in 3D object insertion for augmented reality

Mixing Modalities of 3D Sketching and Speech for Interactive Model Retrieval in Virtual Reality

Sketch and speech are intuitive interaction methods that convey complementary information and have been independently used for 3D model retrieval in virtual environments. While sketch has been shown to be an effective retrieval method, not all collections are easily navigable using this modality alone. We design a new challenging database for sketch comprised of 3D chairs where each of the components (arms, legs, seat, back) are independently colored. To overcome this, we implement a multimodal interface for querying 3D model databases within a virtual environment. We base the sketch on the state-of-the-art for 3D Sketch Retrieval, and use a Wizard-of-Oz style experiment to process the voice input. In this way, we avoid the complexities of natural language processing which frequently requires fine-tuning to be robust. We conduct two user studies and show that hybrid search strategies emerge from the combination of interactions, fostering the advantages provided by both modalities

Neural BRDF Representation and Importance Sampling

Controlled capture of real-world material appearance yields tabulated sets of highly realistic reflectance data. In practice, however, its high memory footprint requires compressing into a representation that can be used efficiently in rendering while remaining faithful to the original. Previous works in appearance encoding often prioritized one of these requirements at the expense of the other, by either applying high-fidelity array compression strategies not suited for efficient queries during rendering, or by fitting a compact analytic model that lacks expressiveness. We present a compact neural network-based representation of BRDF data that combines high-accuracy reconstruction with efficient practical rendering via built-in interpolation of reflectance. We encode BRDFs as lightweight networks, and propose a training scheme with adaptive angular sampling, critical for the accurate reconstruction of specular highlights. Additionally, we propose a novel approach to make our representation amenable to importance sampling: rather than inverting the trained networks, we learn to encode them in a more compact embedding that can be mapped to parameters of an analytic BRDF for which importance sampling is known. We evaluate encoding results on isotropic and anisotropic BRDFs from multiple real-world datasets, and importance sampling performance for isotropic BRDFs mapped to two different analytic models

Image-based Remapping of Material Appearance

Digital 3D content creation requires the ability to exchange assets across multiple software applications. For many 3D asset types, standard formats and interchange conventions are available. For material definitions, however, inter-application exchange is still hampered by different software packages supporting different BRDF models. To make matters worse, even if nominally identical BRDF models are supported, these often differ in their implementation, due to optimisations and safeguards in individual renderers. To facilitate appearance-preserving translation between different BRDF models whose precise implementation is not known (arguably the standard case with commercial systems), we propose a robust translation scheme which leaves BRDF evaluation to the targeted rendering system, and which expresses BRDF similarity in image space. As we will show, even naïve applications of a nonlinear fit which uses such an image space residual metric work well in some cases; however, it does suffer from instabilities for certain material parameters. We propose strategies to mitigate these instabilities and perform reliable parameter remappings between differing BRDF definitions. We report on experiences with this remapping scheme, both with respect to robustness and visual differences of the fits

Enseñanza de la capacidad eléctrica por analogía con un cilindro de gas natural comprimido

Introducimos una analogía que intenta ayudar en la enseñanza del concepto de capacidad eléctrica. La misma está destinada, principalmente, a alumnos y alumnas de nivel medio y del ciclo básico universitario con dificultades en el aprendizaje de conceptos abstractos como éste, y se basa en la comparación del capacitor con un recipiente de gas natural comprimido (GNC) como el que utilizan muchos vehículos en la actualidad. Este modelo puede facilitar la comprensión de la relación entre la carga eléctrica adquirida por un capacitor y la diferencia de potencial aplicada entre sus placas. Por otra parte, el contexto gaseoso, más familiar para la mayoría del alumnado, ayuda a comprender muchas situaciones en las que participan dos capacitores, las que tradicionalmente presentan dificultades para el aprendizaje