  Accurately estimating the 6D pose of objects is crucial for many
applications, such as robotic grasping, autonomous driving, and augmented
reality. However, this task becomes more challenging in poor lighting
conditions or when dealing with textureless objects. To address this issue,
depth images are becoming an increasingly popular choice due to their
invariance to a scene's appearance and the implicit incorporation of essential
geometric characteristics. However, fully leveraging depth information to
improve the performance of pose estimation remains a difficult and
under-investigated problem. To tackle this challenge, we propose a novel
framework called SwinDePose, that uses only geometric information from depth
images to achieve accurate 6D pose estimation. SwinDePose first calculates the
angles between each normal vector defined in a depth image and the three
coordinate axes in the camera coordinate system. The resulting angles are then
formed into an image, which is encoded using Swin Transformer. Additionally, we
apply RandLA-Net to learn the representations from point clouds. The resulting
image and point clouds embeddings are concatenated and fed into a semantic
segmentation module and a 3D keypoints localization module. Finally, we
estimate 6D poses using a least-square fitting approach based on the target
object's predicted semantic mask and 3D keypoints. In experiments on the
LineMod and Occlusion LineMod datasets, SwinDePose outperforms existing
state-of-the-art methods for 6D object pose estimation using depth images. This
demonstrates the effectiveness of our approach and highlights its potential for
improving performance in real-world scenarios. Our code is at
https://github.com/zhujunli1993/SwinDePose.
