Neural Reality Capture

Neural reality capture is the production craft of reconstructing or rendering a real subject from many overlapping observations. In this skill, the deliverable is one of three families:

• a photogrammetric mesh with textures, usually for game, VFX, GIS, cultural heritage, ecommerce, or DCC handoff;
• a NeRF or related neural radiance field, usually for high-quality novel-view rendering from known camera poses;
• a 3D Gaussian splat scene, usually for real-time free-viewpoint playback with radiance-field appearance.

Stay provider-independent. Choose tools by evidence, constraints, and handoff needs, not by brand habit. Do not promise metrology unless the request includes surveyed control, calibrated capture, uncertainty reporting, and a qualified measurement workflow. Do not expand into text-to-3D, generative resculpting, animation rigging, material lookdev beyond captured handoff, or broad finishing work that belongs downstream.

Evidence Labels

Use these labels in plans, reviews, and troubleshooting:

• Paper fact: a claim from a peer-reviewed paper or original technical report, such as the original NeRF or 3D Gaussian Splatting papers.
• Standard fact: a claim from a specification or standard, such as Khronos glTF or OpenUSD documentation.
• Official documentation fact: a claim from tool, capture, or production documentation maintained by the relevant organization.
• Empirical observation: a result from a documented test or reconstruction run.
• Production heuristic: a practical rule that usually improves capture or handoff, but is not a guarantee.