factorsin360

factorsin360 is a software framework intended for factor-based analysis of 360-degree data, including panoramic imagery, immersive video, and sensor streams captured with omnidirectional rigs. The framework is designed to operate on data expressed in spherical coordinates or common projection formats, enabling researchers to identify latent factors that account for variation across space and time in full-sphere observations.

Its architecture emphasizes modularity and interoperability. The core data model supports time-stamped, multi-channel spherical data; projection utilities convert between equirectangular, cubemap, and other sphere-aware representations; and solver backends implement a range of factorization techniques, from principal component analysis to tensor and non-negative factorization. A Python API and accompanying visualization tools facilitate experimentation, inspection of factors, and interactive exploration of results.

Applications span computer vision, virtual reality and augmented reality content analysis, geospatial data fusion, and cognitive science experiments that collect 360-degree stimuli. The project aims to provide reproducible workflows, easy integration with existing pipelines, and clear documentation for researchers and developers.

Development and reception: factorsin360 is an open-source initiative maintained by a growing community of contributors. Ongoing work focuses on expanding algorithm support, improving scalability, and adding benchmarks. Limitations include higher computational demands on large, high-resolution datasets and the challenge of choosing appropriate factor models for spherical data.

See also: 360-degree video, spherical projection, factor analysis, dimensionality reduction, tensor factorization.