Camouflage, a survival strategy perfected by nature, enables organisms to evade detection by blending seamlessly into their surroundings across multiple viewpoints. Replicating this remarkable ability in artificial settings remains a formidable challenge. While recent computational methods have advanced 2D camouflage, extending concealment into 3D is far more difficult: a single texture must reconcile drastically varying backgrounds, making effective camouflage across all views highly challenging. Prior approaches attempt to address this by designing multi-view textures, but their appearance representations are too simplistic to cope with strongly conflicting backgrounds and fail to account for physical light transport, resulting in breakdowns under realistic illumination. We introduce a new paradigm that formulates 3D camouflage as a multiview inverse rendering problem. Instead of treating concealment as texture synthesis, we directly optimize appearance within a physically based rendering framework, explicitly modeling reflections, shadows, refractions, and other complex light interactions. Our approach expands the solution space by combining BSDF-based material representation with anamorphic surface displacement, enabling camouflage that adapts naturally to both viewpoint and illumination changes. Through tailored loss functions and optimization strategies, our method produces results that are not only perceptually convincing but also physically consistent, marking a significant step toward practical, real-world 3D camouflage. Experiments demonstrate that our physically grounded formulation achieves robust concealment across diverse viewpoints and lighting scenarios, substantially outperforming prior image-based methods.

Abstract This paper presents a novel fully automated method for generating view‐independent abstract wire art from 3D models. The main challenge in creating line art is to strike a balance among abstraction, structural clarity, 3D perception, and consistent aesthetics from different viewpoints. Many existing approaches have been proposed, including extracting wire art from mesh, reconstructing it from pictures, etc. But they all suffer from the fact that the wires are usually very unorganized and cumbersome and usually can only guarantee the observation effect of specific viewpoints. To overcome these problems, we propose a paradigm shift: instead of predicting the line segments directly, we consider the generation of wire art as an optimization‐driven manifold‐fitting problem. Thus we can abstract/generalize the 3D model while retaining the key properties necessary for appealing line art, including structural topology and connectivity, and maintain the three‐dimensionality of the line art with a multi‐perspective view. Experimental results show that our view‐independent method outperforms previous methods in terms of line simplicity, shape fidelity, and visual consistency.

This paper addresses a challenging and novel problem in 2D shape cloud visualization: arranging irregular 2D shapes on an irregular canvas to minimize gaps and overlaps while emphasizing critical shapes by displaying them in larger sizes. The concept of a shape cloud is inspired by word clouds, which are widely used in visualization research to aesthetically summarize textual datasets by highlighting significant words with larger font sizes. We extend this concept to images, introducing shape clouds as a powerful and expressive visualization tool, guided by the principle that "a picture is worth a thousand words. Despite the potential of this approach, solutions in this domain remain largely unexplored." To bridge this gap, we develop a 2D shape cloud collage framework that compactly arranges 2D shapes, emphasizing important objects with larger sizes, analogous to the principles of word clouds. This task presents unique challenges, as existing 2D shape layout methods are not designed for scalable irregular packing. Applying these methods often results in suboptimal layouts, such as excessive empty spaces or inaccurate representations of the underlying data. To overcome these limitations, we propose a novel layout framework that leverages recent advances in differentiable optimization. Specifically, we formulate the irregular packing problem as an optimization task, modeling the object arrangement process as a differentiable pipeline. This approach enables fast and accurate end-to-end optimization, producing high-quality layouts. Experimental results show that our system efficiently creates visually appealing and high-quality shape clouds on arbitrary canvas shapes, outperforming existing methods.

Image collage is a very useful tool for visualizing an image collection. Most of the existing methods and commercial applications for generating image collages are designed on simple shapes, such as rectangular and circular layouts. This greatly limits the use of image collages in some artistic and creative settings. Although there are some methods that can generate irregularly-shaped image collages, they often suffer from severe image overlapping and excessive blank space. This prevents such methods from being effective information communication tools. In this article, we present a shape slicing algorithm and an optimization scheme that can create image collages of arbitrary shapes in an informative and visually pleasing manner given an input shape and an image collection. To overcome the challenge of irregular shapes, we propose a novel algorithm, called Shape-Aware Slicing, which partitions the input shape into cells based on medial axis and binary slicing tree. Shape-Aware Slicing,which is designed specifically for irregular shapes, takes human perception and shape structure into account to generate visually pleasing partitions. Then, the layout is optimized by analyzing input images with the goal of maximizing the total salient regions of the images. To evaluate our method, we conduct extensive experiments and compare our results against previous work. The evaluations show that our proposed algorithm can efficiently arrange image collections on irregular shapes and create visually superior results than prior work and existing commercial tools.

Noise exposure has been the emerging environmental factor for human health. Yet an accurate and large-scale sound monitoring network is not available due to the expense of high-quality professional sound level meters and poorly-calibrated low-cost noise sensors. In this work, we propose a participatory sound meter calibration using smartphones. The system employs a low-cost and open-sourced calibration station to conduct side-by-side sound measurements, and all the measurement data are uploaded to the open data portal to build calibration models for different phone brands and models. We show that, using our calibration models, the MAE of calibration performance can be reduced significantly from 12.4 dbA to 2.8 dbA for the same device and 3.3 dbA for the other device of the same phone model. The results of this study can benefit crowdsourcing-based large-scale sound measurements and facilitate noise exposure, public health, and smart city researches in the future.

No abstract available.