FAFA: Frequency-Aware Flow-Aided Self-Supervision for Underwater Object Pose Estimation
This addresses the difficulty of underwater object pose estimation for unmanned underwater vehicles, offering a practical solution by eliminating the need for costly real annotations, though it is incremental as it builds on existing self-supervised and flow-based methods.
The paper tackles the problem of 6D pose estimation for underwater objects, which is challenging due to environmental factors like degraded illumination and blurring, and introduces FAFA, a self-supervised framework that achieves significant performance improvements on benchmarks without needing real pose annotations.
Although methods for estimating the pose of objects in indoor scenes have achieved great success, the pose estimation of underwater objects remains challenging due to difficulties brought by the complex underwater environment, such as degraded illumination, blurring, and the substantial cost of obtaining real annotations. In response, we introduce FAFA, a Frequency-Aware Flow-Aided self-supervised framework for 6D pose estimation of unmanned underwater vehicles (UUVs). Essentially, we first train a frequency-aware flow-based pose estimator on synthetic data, where an FFT-based augmentation approach is proposed to facilitate the network in capturing domain-invariant features and target domain styles from a frequency perspective. Further, we perform self-supervised training by enforcing flow-aided multi-level consistencies to adapt it to the real-world underwater environment. Our framework relies solely on the 3D model and RGB images, alleviating the need for any real pose annotations or other-modality data like depths. We evaluate the effectiveness of FAFA on common underwater object pose benchmarks and showcase significant performance improvements compared to state-of-the-art methods. Code is available at github.com/tjy0703/FAFA.