Vector graphics provide continuous and often even smooth geometric representations of images. While recent approaches to automatically vectorize images lead to relatively good results, they typically leave ample room for improvement: the geometry and color of the vector graphics primitives can be further (automatically) optimized. We propose a novel method that generates high-quality vectorizations based on optimizing input curved triangle meshes (optionally with mesh colors). To overcome the key challenge of establishing a differentiable mapping between the input parameters, i.e., geometry and (mesh) colors of the gradient mesh, and the difference between the vectorized and input image, we treat the input image as a continuous bilinear interpolatory spline and employ Monte Carlo integration. We test our algorithm on various images and show that it can effectively and efficiently improve the quality of an initial vectorization.
Chilling injury (CI) is a major postharvest physiological disorder in fruits, causing quality degradation and economic losses during low-temperature storage. While physically-based methods exist for simulating plant deformation, they are computationally intensive and not optimized for capturing the subtle, spatially distributed symptoms of CI, such as browning, pitting, and wrinkling. In this paper, we propose a biologically-informed, texture-based framework for dynamic CI simulation that links biological symptom progression to visual representation. Browning and pitting are modeled using a texture-based de-chilling technique driven by a Logistic model of the chilling injury index (CII), with a histogram-matching-based algorithm ensuring alignment between simulated symptoms and CII values. Wrinkling is simulated by combining kinetic models of water loss with bump maps generated using Worley noise, which approximate the quasi-random yet locally continuous surface deformations caused by epidermal shrinkage. The proposed framework efficiently integrates biologically-driven modeling, dynamic texture evolution, and water-loss-induced surface deformation, producing realistic CI simulations without high-resolution meshes. It applies to multiple fruit types — including tropical climacteric (banana), Solanaceous (tomato), Cucurbit (cucumber), and Citrus (orange, lemon) — offering an effective approach for visualizing CI progression.
Cartesian mesh-based fluid simulation methods are gaining popularity due to their fully automated mesh generation capabilities for geometries without repair. The performance and flexibility of Cartesian mesh generation significantly influence their application across various fields. This study introduces an efficient adaptive Cartesian mesh generation framework directly for arbitrary geometries. Initially, we propose a robust, high-quality build-in tessellation method and compute proximity. Subsequently, we design a hierarchical storage method combined with binary search for efficient intersection determination. To enhance flexibility, a fully unstructured data type and compressed data representation are established. Finally, we develop a four-step refinement mechanism to achieve geometric adaptation and smooth transitions effectively. The robustness and efficiency of the approach were validated through typical case studies, demonstrating that the mesh generation process for complex models can reach speeds of up to 105 cells per second, which presents significant potential to address the challenges of real-time simulations.
Applications in fields such as fluid mechanics, video games and image processing frequently involve the simulation of 3D objects with spherical topology, with a surface quantity that varies with the geometry and/or according to some surface partial differential equation. However, when the geometry undergoes continuous deformations, significant distortions in the surface point distribution may arise. This can lead to aliasing effects and numerical instability, reducing the overall accuracy of the simulation.To address this issue, we introduce a novel tool to improve the efficiency of a spectral reparametrization procedure able to ensure the optimal representation of such 3D objects and surface quantities, even when dealing with long time simulations. This new strategy makes the reparametrization technique fully adaptive, selecting only the frequencies that are needed to represent a given surface, improving the efficiency of the algorithm, preventing degradation in the quality of the simulation and enhancing the overall stability.
In this paper we present a lattice structure optimization approach by leveraging the capabilities of neural networks for implicit geometry representation. We employ the Deep Signed Distance Field (DeepSDF) method, where a continuous and low-dimensional latent space is introduced to encode the geometric information. In contrast to traditional topology optimization methods, this allows the restriction of the design space to specific geometries. In our case, the latent space is used to represent the geometry of different unit cells, that are stacked to form a lattice structure. Moreover, continuously varying the latent vector over the structure allows a functional grading and optimization. Unlike other lattice-structure optimization methods, we neither assume a large separation of scale nor periodicity. Instead, we perform a full-scale finite element analysis at each optimization step. The required mesh is obtained by a differentiable extension of the dual marching cubes algorithm, which enables gradient-based optimization.
