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.

使用深度形状表示，在单个图像中使用可学习的 Anchored Unsigned Distance Function（AnchorUDF）表示 3D 服装模型，通过预测无符号距离场（UDF）来实现开放服装表面建模和更好的控制拓扑结构，使用像素对齐的本地图像特征和一组锚点来富化查询点的 3D 位置特征，实验证明 AnchorUDF 在单视图 3D 服装重建方面具有最先进的性能。