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.

翻译：无符号距离场（UDFs）能够表示具有复杂拓扑结构的模型，但从这些场中提取精确的零水平集面临着重大挑战，尤其是在保持拓扑精度和捕捉精细几何细节方面。为了克服这些问题，我们提出了DCUDF2，这是对当前最先进方法DCUDF的改进，用于从UDFs中提取零水平集。我们的方法采用了一种精度感知的损失函数，并通过自适应权重进行增强，从而显著提升了几何质量。我们还提出了一种拓扑校正策略，降低了对超参数的依赖，提高了方法的鲁棒性。此外，我们开发了利用自适应权重的新操作，以提高运行时效率。在多个数据集上进行的大量表面提取实验表明，DCUDF2在几何保真度和拓扑精度方面均优于DCUDF及现有方法。我们将公开源代码。