NeRF
xtan can be used with NeRF-based workflows where spatial tracking, stereo vision processing, and geometry-aware perception may support neural scene reconstruction, spatial capture systems, and experimental 3D environment modeling.
Neural scene representation
Neural Radiance Fields (NeRF) are widely used in modern computer vision and graphics pipelines for reconstructing photorealistic scenes from image observations. xtan can be used to explore stereo vision pipelines, spatial tracking systems, and geometry-aware perception tools integrated with NeRF-based workflows.
Potential for 3D reconstruction and spatial capture
Many research environments use NeRF to reconstruct three-dimensional scenes from multi-view image datasets. Motion-aware tracking may support experimental perception systems, spatial capture pipelines, and scene reconstruction workflows built around vision-based sensing and multi-view geometry.
Why xtan can be relevant
xtan focuses on stereo vision, geometry-first interaction, and practical spatial systems. Within NeRF ecosystems this may support perception pipelines, spatial mapping tools, and reconstruction experiments built around vision-based depth estimation and structured geometry interpretation.