Identifying the camera pose for a given image is a challenging problem with
applications in robotics, autonomous vehicles, and augmented/virtual reality.
Lately, learning-based methods have shown to be effective for absolute camera
pose estimation. However, these methods are not accurate when generalizing to
different domains. In this paper, a domain adaptive training framework for
absolute pose regression is introduced. In the proposed framework, the scene
image is augmented for different domains by using generative methods to train
parallel branches using Barlow Twins objective. The parallel branches leverage
a lightweight CNN-based absolute pose regressor architecture. Further, the
efficacy of incorporating spatial and channel-wise attention in the regression
head for rotation prediction is investigated. Our method is evaluated with two
datasets, Cambridge landmarks and 7Scenes. The results demonstrate that, even
with using roughly 24 times fewer FLOPs, 12 times fewer activations, and 5
times fewer parameters than MS-Transformer, our approach outperforms all the
CNN-based architectures and achieves performance comparable to
transformer-based architectures. Our method ranks 2nd and 4th with the
Cambridge Landmarks and 7Scenes datasets, respectively. In addition, for
augmented domains not encountered during training, our approach significantly
outperforms the MS-transformer. Furthermore, it is shown that our domain
adaptive framework achieves better performance than the single branch model
trained with the identical CNN backbone with all instances of the unseen
distribution.

提出了一种面向绝对姿态回归的领域自适应训练框架，基于生成方法实现场景图像的不同领域的数据增强，并在回归头中探究了空间和通道注意力对旋转预测的有效性。结果表明，该方法在效率和性能方面都优于基于 CNN 的架构，并可适用于不同领域。