发表机构
Federal University of ABC (UFABC)（ABC联邦大学（UFABC））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对复杂有机物体提取和渲染纹理网格的挑战，提出基于3D高斯喷溅参数空间的无网格域随机化框架，用两个独立扰动管道合成随机训练数据集，为复杂几何形状生成强大数据集提供无网格替代方法。
AI 中文摘要
域随机化（DR）是弥合模拟到现实差距的标准技术，但传统DR管道依赖由多边形网格驱动的经典计算机图形渲染。对于复杂有机物体，如昆虫标本，提取和渲染纹理网格具有挑战性。为解决此问题，我们提出了一种在3D高斯喷溅（3DGS）参数空间上运行的无网格DR框架。我们的方法采用两个独立的扰动管道来合成随机训练数据集。首先，光度DR管道通过调制球谐（SH）系数来改变烘焙照明和颜色平衡。其次，过程DR管道通过用3D空间噪声替换原始纹理来分离物体的几何形状。最后，使用光栅化引擎将这些扰动的辐射场合成在随机变化的背景上。我们的参数操作提供了一种无网格替代方法，可为复杂几何形状生成强大的数据集。
英文摘要
Domain Randomization (DR) is a standard technique for closing the Sim-to-Real gap, yet traditional DR pipelines rely on classical computer graphics rendering driven by polygon meshes. For complex organic subjects, such as insect specimens, extracting and rendering textured meshes is challenging. To address this issue, we propose a meshless DR framework that operates on the parameter space of 3D Gaussian Splatting (3DGS). Our method employs two independent perturbation pipelines to synthesize randomized training datasets. First, a Photometric DR pipeline alters the baked illumination and color balance by modulating the Spherical Harmonics (SH) coefficients. Second, a Procedural DR pipeline isolates the subject's geometric shape by replacing its original textures with 3D spatial noise. Finally, these perturbed radiance fields are composited over stochastically varied backgrounds using a rasterization engine. Our parameter manipulation provides a meshless alternative for generating robust datasets for complex geometries.
Comments10 pages, 4 figures
