VAEPose: 6D Pose Estimation with Visual Appearance Enhancement for Low-Light Conditions

6D pose estimation is a key technology in the field of computer vision, and has great application potential in the fields of virtual reality, augmented reality, robot operation, and intelligent driving. When using deep neural network technology to accomplish this advanced vision task, the quality of the data is demanding. Currently, the task achieves excellent results in normal lighting conditions, but the task presents serious challenges due to the significant degradation of data quality in low-light conditions. The results are not satisfactory when the object pose is directly estimated using a one-stage processing mode in low-light conditions. Therefore, in view of this observation, we choose a two-stage processing mode to solve the challenges. In this work, we propose a Low-light Pose Estimation Network based on the Visual Appearance Enhancement module (VAEPose). Among them, the Low-light Appearance Enhancement Module (LAEM) is designed to improve data quality and show clearer details, helping the network model to be more adaptable to light and less dependent on data. In the LAEM module, an attention mechanism is introduced to achieve the fusion of channel and spatial attention of the extracted RGB features, resulting in finer color and texture information. The Virtual Low-light Dataset (VLD) used in the experiment is created by us in virtual environment. Experiments verify the effectiveness of the proposed method.