While single-view 3D reconstruction has made significant progress benefiting from deep shape representations in recent years, garment reconstruction is still not solved well due to open surfaces, diverse topologies and complex geometric details. In this paper, we propose a novel learnable Anchored Unsigned Distance Function (AnchorUDF) representation for 3D garment reconstruction from a single image. AnchorUDF represents 3D shapes by predicting unsigned distance fields (UDFs) to enable open garment surface modeling at arbitrary resolution. To capture diverse garment topologies, AnchorUDF not only computes pixel-aligned local image features of query points, but also leverages a set of anchor points located around the surface to enrich 3D position features for query points, which provides stronger 3D space context for the distance function. Furthermore, in order to obtain more accurate point projection direction at inference, we explicitly align the spatial gradient direction of AnchorUDF with the ground-truth direction to the surface during training. Extensive experiments on two public 3D garment datasets, i.e., MGN and Deep Fashion3D, demonstrate that AnchorUDF achieves the state-of-the-art performance on single-view garment reconstruction.

翻译：虽然从近年来的深度外观 3D 重建从深度外观中取得了显著进展,但由于开放表面、各种地形和复杂的几何细节,服装重建仍没有很好解决。在本文件中,我们提议从一个图像中为3D 服装重建提供一个新颖的可学习的 Anchored untign Constreet Forme(AnchorUDF) 代表3D 形状。 anchorUDF 通过预测未签名的距离场( UDF) 代表3D 形状, 以便能够以任意分辨率为开放的服装表面建模。 为了捕捉不同的服装表面结构, AnchorUDF 不仅计算出查询点与本地相近的图像特征, 而且还利用地表周围的一组固定点来丰富3D 位置的查询点的定位特征, 为远程功能提供更强的 3D 空间环境。 此外,为了获得更准确的点预测方向, 我们明确地将 AnchorUDF 的空间梯度方向与培训期间的地面方向相向表面划一。 在两个公开的3D制衣数据集上进行广泛的实验, i.e, MGNGN和深Fart-fash-s-s-simax-first 3 的状态上表现显示。