BrepGaussian：从多视角图像中通过高斯点划进行CAD重建
BrepGaussian: CAD reconstruction from Multi-View Images with Gaussian Splatting
AI总结：
本文提出BrepGaussian框架，通过学习2D图像中的3D参数化表示，实现从多视角图像中重建CAD模型，其核心方法是结合可学习特征的高斯点划渲染器和特定拟合策略，有效分离了几何重建与特征学习。
摘要：
边界表示（B-Rep）将3D实体表示为其显式边界：修剪的角点、边和面。从无结构数据中恢复B-Rep表示是计算机视觉和图形学中的挑战性且有价值的任务。尽管深度学习的最新进展大幅提高了3D形状几何的恢复，但仍然依赖于密集且干净的点云，并难以泛化到新形状。我们提出B-Rep高斯点划（BrepGaussian），一种新的框架，从2D图像中学习3D参数化表示。我们采用具有可学习特征的高斯点划渲染器，随后是特定的拟合策略。为了分离几何重建和特征学习，我们引入了两阶段学习框架，首先捕获几何和边缘，然后细化补丁特征以实现干净的几何和一致的实例表示。广泛的实验表明，我们的方法在性能上优于现有最先进方法。
英文摘要：
The boundary representation (B-Rep) models a 3D solid as its explicit boundaries: trimmed corners, edges, and faces. Recovering B-Rep representation from unstructured data is a challenging and valuable task of computer vision and graphics. Recent advances in deep learning have greatly improved the recovery of 3D shape geometry, but still depend on dense and clean point clouds and struggle to generalize to novel shapes. We propose B-Rep Gaussian Splatting (BrepGaussian), a novel framework that learns 3D parametric representations from 2D images. We employ a Gaussian Splatting renderer with learnable features, followed by a specific fitting strategy. To disentangle geometry reconstruction and feature learning, we introduce a two-stage learning framework that first captures geometry and edges and then refines patch features to achieve clean geometry and coherent instance representations. Extensive experiments demonstrate the superior performance of our approach to state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
