We present BiGS, an image-based novel view synthesis technique designed to model and render 3D objects with surface and volumetric materials under dynamic illumination, achieving real-time relighting of 3D objects with the rasterization algorithm of Gaussian Splatting. Our method represents light- and view-dependent scattering via bidirectional spherical harmonics that does not use a specific surface normal-related reflectance function, making it more compatible with volumetric representations like Gaussian splatting, where the normals are undefined. We demonstrate our method by reconstructing and rendering objects with complex materials on synthetic and captured One-Light-At-a-Time (OLAT) datasets, showcasing various photorealistic appearances our method captures and the real-time performance.

Thermal imaging, as a promising approach for scalable and robust scene perception, is invaluable for many applications in various fields, such as architecture and building physics. Despite many recent works having demonstrated their capability to incorporate thermal images into radiance field methods, they typically do not explicitly model how radiation interacts and reflects within the scene before reaching the camera, which is essential for inferring thermal physics and properties of objects in a scene. Using Gaussian primitives as the scene representation, our method estimates surface temperature and material properties to generate infrared renderings that closely match the input images. Taking inspirations from radiosity and hemicube rasterization, our method decomposes the outgoing radiation from each Gaussian primitive into two parts: self-emission and reflection originating from other primitives and the environment. This formulation allows us to simulate radiation under novel heating conditions and to find the best-fit temperature and material parameters given thermal images. The method is verified using both synthetic and real capture datasets.

Abstract Embroidery is a long‐standing and high‐quality approach to making logos and images on textiles. Nowadays, it can also be performed via automated machines that weave threads with high spatial accuracy. A characteristic feature of the appearance of the threads is a high degree of anisotropy. The anisotropic behavior is caused by depositing thin but long strings of thread. As a result, the stitched patterns convey both color and direction. Artists leverage this anisotropic behavior to enhance pure color images with textures, illusions of motion, or depth cues. However, designing colorful embroidery patterns with prescribed directionality is a challenging task, one usually requiring an expert designer. In this work, we propose an interactive algorithm that generates machine‐fabricable embroidery patterns from multi‐chromatic images equipped with user‐specified directionality fields. We cast the problem of finding a stitching pattern into vector theory. To find a suitable stitching pattern, we extract sources and sinks from the divergence field of the vector field extracted from the input and use them to trace streamlines. We further optimize the streamlines to guarantee a smooth and connected stitching pattern. The generated patterns approximate the color distribution constrained by the directionality field. To allow for further artistic control, the trade‐off between color match and directionality match can be interactively explored via an intuitive slider. We showcase our approach by fabricating several embroidery paths.

Abstract We study structural rigidity for assemblies with mechanical joints. Existing methods identify whether an assembly is structurally rigid by assuming parts are perfectly rigid. Yet, an assembly identified as rigid may not be that “rigid” in practice, and existing methods cannot quantify how rigid an assembly is. We address this limitation by developing a new measure, worst‐case rigidity, to quantify the rigidity of an assembly as the largest possible deformation that the assembly undergoes for arbitrary external loads of fixed magnitude. Computing worst‐case rigidity is non‐trivial due to non‐rigid parts and different joint types. We thus formulate a new computational approach by encoding parts and their connections into a stiffness matrix, in which parts are modeled as deformable objects and joints as soft constraints. Based on this, we formulate worst‐case rigidity analysis as an optimization that seeks the worst‐case deformation of an assembly for arbitrary external loads, and solve the optimization problem via an eigenanalysis. Furthermore, we present methods to optimize the geometry and topology of various assemblies to enhance their rigidity, as guided by our rigidity measure. In the end, we validate our method on a variety of assembly structures with physical experiments and demonstrate its effectiveness by designing and fabricating several structurally rigid assemblies.