  Unsupervised domain adaptation (UDA) is a critical challenge in the field of
point cloud analysis. Previous works tackle the problem either by feature
extractor adaptation to enable a shared classifier to distinguish
domain-invariant features, or by classifier adaptation to evolve the classifier
to recognize target-styled source features to increase its adaptation ability.
However, by learning domain-invariant features, feature extractor adaptation
methods fail to encode semantically meaningful target-specific information,
while classifier adaptation methods rely heavily on the accurate estimation of
the target distribution. In this work, we propose a novel framework that deeply
couples the classifier and feature extractor adaption for 3D UDA, dubbed
Progressive Classifier and Feature Extractor Adaptation (PCFEA). Our PCFEA
conducts 3D UDA from two distinct perspectives: macro and micro levels. On the
macro level, we propose a progressive target-styled feature augmentation (PTFA)
that establishes a series of intermediate domains to enable the model to
progressively adapt to the target domain. Throughout this process, the source
classifier is evolved to recognize target-styled source features (\ie,
classifier adaptation). On the micro level, we develop an intermediate domain
feature extractor adaptation (IDFA) that performs a compact feature alignment
to encourage the target-styled feature extraction gradually. In this way, PTFA
and IDFA can mutually benefit each other: IDFA contributes to the distribution
estimation of PTFA while PTFA constructs smoother intermediate domains to
encourage an accurate feature alignment of IDFA. We validate our method on
popular benchmark datasets, where our method achieves new state-of-the-art
performance. Our code is available at https://github.com/xiaoyao3302/PCFEA.
