📄 中文摘要
非破坏性提取目标内部部件（如电池和电机）在回收和处置场所至关重要。由于产品多样性和缺乏拆解程序信息，确定切割位置变得具有挑战性。研究提出了一种非破坏性提取目标内部部件的方法，该方法通过观察切割表面迭代估计内部结构，并基于估计结果制定切割计划。关键要求是从部分观察中估计目标部件存在的概率。然而，针对这一任务学习条件生成模型面临挑战，主要是由于三维形状表示的高维性。该方法为解决复杂的拆解过程提供了一种新的思路，能够有效提高拆解效率和安全性。
VoxelDiffusionCut: 非破坏性内部部件提取方法通过迭代切割与结构估计
📄 English Summary
VoxelDiffusionCut: Non-destructive Internal-part Extraction via Iterative Cutting and Structure Estimation
The non-destructive extraction of target internal parts, such as batteries and motors, is crucial at recycling and disposal sites. The diversity of products and the lack of information on disassembly procedures make it challenging to determine where to cut. A method for non-destructive extraction of target internal parts is proposed, which iteratively estimates the internal structure from observed cutting surfaces and formulates cutting plans based on the estimation results. A key requirement is to estimate the probability of the target part's presence from partial observations. However, learning conditional generative models for this task is challenging due to the high dimensionality of 3D shape representations. This method offers a new approach to address the complexities of disassembly processes, potentially enhancing efficiency and safety.