AI & ML
Representations of 3D Rotations: Mathematical Foundations and Comparative Analysis
arXiv cs.RO0 views
arXiv:2605.08086v1 Announce Type: cross Abstract: Rotation representations are foundational in fields such as computer graphics, robotics, and machine learning, where precise and efficient modeling of 3D orientations is critical. This paper comprehensively investigates diverse representations of the special orthogonal group $SO(3)$, such as Euler angles, axis-angle vectors, quaternions, rotation matrices, exponential maps, and emerging continuous and probabilistic methods, evaluating their mathematical formulations, continuity, susceptibility to gimbal lock, computational efficiency, storage requirements, interpolation properties, and composition operations, while integrating detailed algebraic insights with practical applications in fields like animation, pose estimation, inertial navigation, 3D shape registration, and neural networks. Empirical evidence highlights quaternions' dominance due to their compactness and computational efficiency, while alternatives like 6D continuous representations and matrix Fisher distributions provide enhanced continuity and uncertainty modeling. Future research could explore hybrid methods and thorough large-scale evaluations to help build a solid foundation for improving rotation representation techniques.
Source:https://arxiv.org/abs/2605.08086PreviouscuRoboV2-Fast: Fast Torque-Limited Collision-Free Motion PlanningNextBeyond Accuracy: Information-Theoretic Decoder Evaluation Under Latent Intent
Related posts
A Bayesian Optimized Biquad Filter-Burg Model for Real-Time EEG Phase Estimation
0arXiv eess.SP
Thought-deciphering via hierarchical EEG-to-text alignment
0arXiv eess.SP
Neuromorphic Decoding and Dynamical Image Reconstruction from Populations of Spiking Retinal Ganglion Cells
0arXiv eess.SP
Benchmarking Sensor-Fault Robustness in Forecasting
0arXiv eess.SP
Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
0arXiv eess.SP
Multi-Threshold Sampling: Signal Space, Sampling Operators, and First Recorded Crossing-Time Distributions
0arXiv eess.SP