{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"RoMa provides differentiable conversions between 3D rotation representations (rotation vectors, quaternions, rotation matrices, Euler angles) and utilities for rotation-space operations in PyTorch, designed for machine learning and gradient-based optimization.","skillfed_tags":["rotation-geometry","pytorch-math","differentiable-computing"],"use_cases":["Train neural networks that predict 3D rotations or rotation-parameterized transformations in computer vision or robotics","Implement gradient-based optimization over rotation space for pose estimation or alignment problems","Convert between rotation representations in a differentiable pipeline without breaking the computational graph","Interpolate smoothly between rotations using spherical interpolation for animation or trajectory generation","Compose and invert rigid transformations (rotation + translation) in batch form for 3D geometry operations"],"what_it_does":"RoMa is a PyTorch library for working with 3D rotations in machine learning pipelines. It handles conversions between common rotation representations\u2014rotation vectors, unit quaternions, rotation matrices, and Euler angles\u2014while maintaining differentiability throughout for backpropagation. The library also provides rotation-space metrics (geodesic distance, cosine angle), quaternion operations, spherical interpolation, and rigid transformation composition.\n\nThe package targets researchers and engineers building neural networks or optimization systems that need to reason about 3D rotations. It abstracts away the mathematical complexity of rotation manifolds and provides utilities like Procrustes orthonormalization and Gram-Schmidt orthogonalization for converting arbitrary matrices to valid rotations. Batch operations are supported naturally across arbitrary dimensions.","worth_installing":"Yes. RoMa is actively maintained, has no known vulnerabilities, installs with minimal friction (two common dependencies), and fills a specific need for differentiable rotation handling in PyTorch. The permissive BSD-3-Clause license poses no barrier. Install it if your project involves 3D rotations, pose estimation, or rotation-aware neural networks."},"id":"roma","links":{"html":"https://skillfed.io/packages/roma","md":"https://skillfed.io/packages/roma.md","pypi":"https://pypi.org/project/roma/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"roma","python_support":"supports_current","summary":"A lightweight library to deal with 3D rotations in PyTorch."},"popularity":{"monthly_downloads":238121,"position":8947,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.6"}