用贝塞尔曲线抽象3D形状，兼顾几何与视觉特征。
WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction
- 分两阶段优化贝塞尔曲线，先粗后细捕捉形状特征。
- 引入局部关键点损失实现特征空间引导，支持用户控制。
- 通过神经SDF损失保持原表面精度，可作变形控制柄。
本文提出WIR3D，一种通过稀疏的三维可视曲线抽象3D形状的方法。通过优化贝塞尔曲线参数，使其从任意视角忠实表示形状的几何结构和显著视觉特征（如纹理）。利用预训练基础模型CLIP的中间激活作为优化引导。优化分为两阶段：第一阶段捕获粗略几何，第二阶段细化特征表示，并采用新颖的局部关键点损失进行空间引导，实现用户对抽象特征的可控性。通过神经SDF损失确保与原始表面的一致性，使曲线可作为直观的变形控制柄。在多种复杂度、几何结构与纹理的3D形状数据集上验证方法有效性，并展示了在特征控制与形状变形等下游任务中的应用。
原文摘要 · Abstract (English)
In this work we present WIR3D, a technique for abstracting 3D shapes through a sparse set of visually meaningful curves in 3D. We optimize the parameters of Bezier curves such that they faithfully represent both the geometry and salient visual features (e.g. texture) of the shape from arbitrary viewpoints. We leverage the intermediate activations of a pre-trained foundation model (CLIP) to guide our optimization process. We divide our optimization into two phases: one for capturing the coarse geometry of the shape, and the other for representing fine-grained features. Our second phase supervision is spatially guided by a novel localized keypoint loss. This spatial guidance enables user control over abstracted features. We ensure fidelity to the original surface through a neural SDF loss, which allows the curves to be used as intuitive deformation handles. We successfully apply our method for shape abstraction over a broad dataset of shapes with varying complexity, geometric structure, and texture, and demonstrate downstream applications for feature control and shape deformation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。