5 papers
From Theory to Throughput: CUDA-Optimized APML for Large-Batch 3D Learning
Sasan Sharifipour, Constantino Álvarez Casado, Manuel Lage Cañellas +1
Loss functions are fundamental to learning accurate 3D point cloud models, yet common choices trade geometric fidelity for computational cost. Chamfer Distance is efficient but per…
Quality-Aware Framework for Video-Derived Respiratory Signals
Nhi Nguyen, Constantino Álvarez Casado, Le Nguyen +2
Video-based respiratory rate (RR) estimation is often unreliable due to inconsistent signal quality across extraction methods. We present a predictive, quality-aware framework that…
3DPCNet: Pose Canonicalization for Robust Viewpoint-Invariant 3D Kinematic Analysis from Monocular RGB cameras
Tharindu Ekanayake, Constantino Álvarez Casado, Miguel Bordallo López
Monocular 3D pose estimators produce camera-centered skeletons, creating view-dependent kinematic signals that complicate comparative analysis in applications such as health and sp…
LiDAR-based Human Activity Recognition through Laplacian Spectral Analysis
Sasan Sharifipour, Constantino Álvarez Casado, Le Nguyen +4
Human Activity Recognition supports applications in healthcare, manufacturing, and human-machine interaction. LiDAR point clouds offer a privacy-preserving alternative to cameras a…
APML: Adaptive Probabilistic Matching Loss for Robust 3D Point Cloud Reconstruction
Sasan Sharifipour, Constantino Álvarez Casado, Mohammad Sabokrou +1
Training deep learning models for point cloud prediction tasks such as shape completion and generation depends critically on loss functions that measure discrepancies between predi…