Modeling surface reflectance is central to connecting optical theory with real-world rendering and fabrication. While analytic BRDFs remain standard in rendering, recent advances in geometric and wave optics have expanded the design space for complex reflectance effects. However, existing wave-optics-based methods are limited to controlling reflectance intensity only, lacking the ability to design full-spectrum, color-dependent BRDFs. In this work, we present the first method for designing and fabricating color BRDFs using a fully differentiable wave optics framework. Our differentiable and memory-efficient simulation framework supports end-to-end optimization of microstructured surfaces under scalar diffraction theory, enabling joint control over both angular intensity and spectral color of reflectance. We leverage grayscale lithography with a feature size of 1.5–2.0 μ m to fabricate 15 BRDFs spanning four representative categories: anti-mirrors, pictorial reflections, structural colors, and iridescences. Compared to prior work, our approach achieves significantly higher fidelity and broader design flexibility, producing physically accurate and visually compelling results. By providing a practical and extensible solution for full-color BRDF design and fabrication, our method opens up new opportunities in structural coloration, product design, security printing, and advanced manufacturing.

We propose a novel framework for real-time acquisition and reconstruction of temporally-varying 3D phenomena with high quality. The core of our framework is a deep neural network, with an encoder that directly maps to the structured illumination during acquisition, a decoder that predicts a 1D density distribution from single-pixel measurements under the optimized lighting, and an aggregation module that combines the predicted densities for each camera into a single volume. It enables the automatic and joint optimization of physical acquisition and computational reconstruction, and is flexible to adapt to different hardware configurations. The effectiveness of our framework is demonstrated on a lightweight setup with an off-the-shelf projector and one or multiple cameras, achieving a performance of 40 volumes per second at a spatial resolution of 1283. We compare favorably with state-of-the-art techniques in real and synthetic experiments, and evaluate the impact of various factors over our pipeline.

We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex appearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance. Our code and data are publicly available at https://GSrelight.github.io/.

Abstract Anticipation skill is important for elite racquet sports players. Successful anticipation allows them to predict the actions of the opponent better and take early actions in matches. Existing studies of anticipation behaviors, largely based on the analysis of in‐lab behaviors, failed to capture the characteristics of in‐situ anticipation behaviors in real matches. This research proposes a data‐driven approach for research on anticipation behaviors to gain more accurate and reliable insight into anticipation skills. Collaborating with domain experts in table tennis, we develop a complete solution that includes data collection, the development of a model to evaluate anticipation behaviors, and the design of a visual analytics system called Tac‐Anticipator. Our case study reveals the strengths and weaknesses of top table tennis players' anticipation behaviors. In a word, our work enriches the research methods and guidelines for visual analytics of anticipation behaviors.