LAUSR

Point-cloud processing for extracting geometric features is difficult due to the highly non-linear rotation variance and measurement noise corrupting the data. To address these challenges, we propose a new architecture, called Dense 3D Geometric Features Extraction And Pose Estimation Using Self-Attention (D3GATTEN), which allows us to extract strong 3D features. Later on these can be used for point-cloud registration, object reconstruction, pose estimation, and tracking. The key contribution of our work is a new architecture that makes use of the self-attention module to extract powerful features. Thoughtful tests were performed on the 3DMatch dataset for point-cloud registration and on TUM RGB-D dataset for pose estimation achieving 98% Feature Matching Recall (FMR). Our results outperformed the existing state-of-the-art in terms of robustness specification for point-cloud alignment and pose estimation. Our code and test data can be accessed at link: https://github.com/tamaslevente/trai/tree/master/d3gatten.