In this paper, an efficient normal estimation and filtering method for depth images acquired by Time-of-Flight (ToF) cameras is proposed. The method is based on a common feature pyramid networks (FPN) architecture. The normal estimation method is called ToFNest, and the filtering method ToFClean. Both of these low-level 3D point cloud processing methods start from the 2D depth images, projecting the measured data into the 3D space and computing a task-specific loss function. Despite the simplicity, the methods prove to be efficient in terms of robustness and runtime. In order to validate the methods, extensive evaluations on public and custom datasets were performed. Compared with the state-of-the-art methods, the ToFNest and ToFClean algorithms are faster by an order of magnitude without losing precision on public datasets.

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In this paper a Time of Flight (ToF) camera specific data processing pipeline is presented, followed by real life applications using artificial intelligence. These applications include use cases such as gesture recognition, movement direction estimation or physical exercises monitoring. The whole pipeline for the body pose estimation is described in details, starting from generating and training phases to the pose estimation and deployment. The final deployment targets were Nvidia Xavier NX and AGX platforms receiving data from an Analog Devices ToF camera.

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-ofthe-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.