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.

本文提出了一种新颖的面向室内三维物体检测的对象层次域对齐（OHDA）框架，其中包括一种面向对象的数据增强策略以有效地使源域数据多样化，并引入了一个由对抗训练分支和伪标记分支组成的两分支自适应框架，以同时达到整体级和类别级的域对齐，通过适应结果的改进在Synthetic数据集3D-FRONT到真实数据集ScanNetV2和SUN RGB-D上分别取得了分别为9.7％和9.1％的mAP25改进。

室内3D物体检测的Syn-to-Real无监督领域自适应