Unsigned distance fields (UDFs) allow for the representation of models with complex topologies, but extracting accurate zero level sets from these fields poses significant challenges, particularly in preserving topological accuracy and capturing fine geometric details. To overcome these issues, we introduce DCUDF2, an enhancement over DCUDF--the current state-of-the-art method--for extracting zero level sets from UDFs. Our approach utilizes an accuracy-aware loss function, enhanced with self-adaptive weights, to improve geometric quality significantly. We also propose a topology correction strategy that reduces the dependence on hyper-parameter, increasing the robustness of our method. Furthermore, we develop new operations leveraging self-adaptive weights to boost runtime efficiency. Extensive experiments on surface extraction across diverse datasets demonstrate that DCUDF2 outperforms DCUDF and existing methods in both geometric fidelity and topological accuracy. We will make the source code publicly available.

本研究解决了从无符号距离场中提取零级集时面临的拓扑准确性和几何细节捕捉的挑战。提出的DCUDF2方法引入了具有自适应权重的准确性感知损失函数，显著提高了几何质量，并通过拓扑修正策略增强了方法的鲁棒性。实验结果表明，DCUDF2在几何保真度和拓扑准确性方面优于现有最先进的方法DCUDF。

DCUDF2：提高从无符号距离场中提取零级集的效率与准确性