Simulation data can be accurately labeled and have been expected to improve
the performance of data-driven algorithms, including object detection. However,
due to the various domain inconsistencies from simulation to reality
(sim-to-real), cross-domain object detection algorithms usually suffer from
dramatic performance drops. While numerous unsupervised domain adaptation (UDA)
methods have been developed to address cross-domain tasks between real-world
datasets, progress in sim-to-real remains limited. This paper presents a novel
Complex-to-Simple (CTS) framework to transfer models from labeled simulation
(source) to unlabeled reality (target) domains. Based on a two-stage detector,
the novelty of this work is threefold: 1) developing fixed-size anchor heads
and RoI augmentation to address size bias and feature diversity between two
domains, thereby improving the quality of pseudo-label; 2) developing a novel
corner-format representation of aleatoric uncertainty (AU) for the bounding
box, to uniformly quantify pseudo-label quality; 3) developing a noise-aware
mean teacher domain adaptation method based on AU, as well as object-level and
frame-level sampling strategies, to migrate the impact of noisy labels.
Experimental results demonstrate that our proposed approach significantly
enhances the sim-to-real domain adaptation capability of 3D object detection
models, outperforming state-of-the-art cross-domain algorithms, which are
usually developed for real-to-real UDA tasks.

提出了一种新颖的复杂到简单框架（CTS），从有标记的仿真域（源）转移模型到无标记的实际域（目标），通过开发固定大小的锚头和 RoI 增强来提高伪标签的质量，并开发角格式表示的 Aleatoric 不确定性（AU）以统一量化伪标签质量，还基于 AU 开发了一个噪声感知的均值教师域适应方法，以及基于 AU 的对象级和帧级采样策略，以迁移噪声标签的影响，实验结果表明，我们提出的方法显著增强了三维物体检测模型的仿真到实际域适应能力，胜过了通常针对实际到实际无监督域适应任务开发的最先进跨域算法。