  The use of synthetic data in indoor 3D object detection offers the potential
of greatly reducing the manual labor involved in 3D annotations and training
effective zero-shot detectors. However, the complicated domain shifts across
syn-to-real indoor datasets remains underexplored. In this paper, we propose a
novel Object-wise Hierarchical Domain Alignment (OHDA) framework for
syn-to-real unsupervised domain adaptation in indoor 3D object detection. Our
approach includes an object-aware augmentation strategy to effectively
diversify the source domain data, and we introduce a two-branch adaptation
framework consisting of an adversarial training branch and a pseudo labeling
branch, in order to simultaneously reach holistic-level and class-level domain
alignment. The pseudo labeling is further refined through two proposed schemes
specifically designed for indoor UDA. Our adaptation results from synthetic
dataset 3D-FRONT to real-world datasets ScanNetV2 and SUN RGB-D demonstrate
remarkable mAP25 improvements of 9.7% and 9.1% over Source-Only baselines,
respectively, and consistently outperform the methods adapted from 2D and 3D
outdoor scenarios. The code will be publicly available upon paper acceptance.
