This paper introduces a new approach based on a coupled representation and a neural volume optimization to implicitly perform 3D shape editing in latent space. This work has three innovations. First, we design the coupled neural shape (CNS) representation for supporting 3D shape editing. This representation includes a latent code, which captures high-level global semantics of the shape, and a 3D neural feature volume, which provides a spatial context to associate with the local shape changes given by the editing. Second, we formulate the coupled neural shape optimization procedure to co-optimize the two coupled components in the representation subject to the editing operation. Last, we offer various 3D shape editing operators, i.e., copy, resize, delete, and drag, and derive each into an objective for guiding the CNS optimization, such that we can iteratively co-optimize the latent code and neural feature volume to match the editing target. With our approach, we can achieve a rich variety of editing results that are not only aware of the shape semantics but are also not easy to achieve by existing approaches. Both quantitative and qualitative evaluations demonstrate the strong capabilities of our approach over the state-of-the-art solutions.

翻译：本文提出了一种基于耦合表征与神经体素优化的新方法，可在隐空间中隐式完成三维形状编辑。本研究有三项创新：首先，设计了耦合神经形状（CNS）表征以支持三维形状编辑，该表征包含捕获形状高层全局语义的隐编码，以及提供空间上下文以关联编辑操作所引发局部形状变化的三维神经特征体素；其次，构建了耦合神经形状优化流程，使表征中的两个耦合组件在编辑操作约束下协同优化；最后，提供了复制、缩放、删除和拖拽等多种三维形状编辑算子，并将每种算子推导为引导CNS优化的目标函数，通过迭代协同优化隐编码与神经特征体素以匹配编辑目标。本方法不仅能生成感知形状语义的丰富编辑结果，且现有方法难以实现此类效果。定量与定性评估均表明，本方法较现有最优方案展现出显著优势。