Title: High Resolution UDF Meshing via Iterative Networks Show Chinese title

Title: 通过迭代网络的高分辨率UDF网格化

Abstract: Unsigned Distance Fields (UDFs) are a natural implicit representation for open surfaces but, unlike Signed Distance Fields (SDFs), are challenging to triangulate into explicit meshes. This is especially true at high resolutions where neural UDFs exhibit higher noise levels, which makes it hard to capture fine details. Most current techniques perform within single voxels without reference to their neighborhood, resulting in missing surface and holes where the UDF is ambiguous or noisy. We show that this can be remedied by performing several passes and by reasoning on previously extracted surface elements to incorporate neighborhood information. Our key contribution is an iterative neural network that does this and progressively improves surface recovery within each voxel by spatially propagating information from increasingly distant neighbors. Unlike single-pass methods, our approach integrates newly detected surfaces, distance values, and gradients across multiple iterations, effectively correcting errors and stabilizing extraction in challenging regions. Experiments on diverse 3D models demonstrate that our method produces significantly more accurate and complete meshes than existing approaches, particularly for complex geometries, enabling UDF surface extraction at higher resolutions where traditional methods fail. Show Chinese abstract

Abstract: 无符号距离场（UDFs）是开放曲面的自然隐式表示，但与有符号距离场（SDFs）不同，将其三角化为显式网格具有挑战性。 这在高分辨率情况下尤其明显，其中神经UDFs表现出更高的噪声水平，这使得捕捉细节变得困难。 目前大多数技术仅在单个体素内进行操作，而没有参考其邻域，导致在UDF模糊或噪声较大的地方出现缺失表面和孔洞。 我们表明，通过进行多次遍历并利用之前提取的表面元素进行推理，可以引入邻域信息来解决这个问题。 我们的主要贡献是一种迭代神经网络，它能够这样做，并通过从越来越远的邻域空间传播信息，在每个体素内逐步提高表面恢复效果。 与单次遍历方法不同，我们的方法在多次迭代中整合新检测到的表面、距离值和梯度，有效地纠正错误并在具有挑战性的区域中稳定提取。 在多种3D模型上的实验表明，与现有方法相比，我们的方法生成的网格更加准确和完整，尤其是在复杂几何结构上，使UDF表面提取能够在传统方法失败的更高分辨率下实现。