Domain gap between synthetic and real data in visual regression (\eg 6D pose
estimation) is bridged in this paper via global feature alignment and local
refinement on the coarse classification of discretized anchor classes in target
space, which imposes a piece-wise target manifold regularization into
domain-invariant representation learning. Specifically, our method incorporates
an explicit self-supervised manifold regularization, revealing consistent
cumulative target dependency across domains, to a self-training scheme (\eg the
popular Self-Paced Self-Training) to encourage more discriminative transferable
representations of regression tasks. Moreover, learning unified implicit neural
functions to estimate relative direction and distance of targets to their
nearest class bins aims to refine target classification predictions, which can
gain robust performance against inconsistent feature scaling sensitive to UDA
regressors. Experiment results on three public benchmarks of the challenging 6D
pose estimation task can verify the effectiveness of our method, consistently
achieving superior performance to the state-of-the-art for UDA on 6D pose
estimation.

本文通过在目标空间的离散化锚点类别的粗分类的全局特征对齐和局部细化来弥合合成数据和实际数据之间视觉回归（例如六维姿态估计）中的领域差距，使其对领域不变表示学习施加了分段目标流形正则化。此外，本方法还将统一的隐式神经函数学习应用于估计目标相对方向和距离的回归任务中，以改善目标分类预测，实验表明，在三个公开基准测试中，​​本文方法在相对于最先进的 UDA 6D 姿势估计方法表现出了卓越的性能。