Neural Renderer

Why build another renderer?

Neural radiance fields are often presented as an impressive output image and a compact equation. The difficult engineering lies between those two things: unreliable camera poses, memory pressure, long feedback loops, and a representation that does not naturally fit the web.

This project treats reconstruction as a complete product pipeline rather than a model demo. A scene begins as an unstructured set of photographs and ends as a progressively streamed experience that can be opened on an ordinary device.

The representation

The scene is a continuous volumetric function:

$$F_\Theta : (x, d) \mapsto (c, \sigma)$$

Position $x$ and direction $d$ produce colour $c$ and density $\sigma$. Rendering integrates those predictions along a camera ray. The current implementation replaces a deep positional network with a compact multiresolution hash grid, spending capacity where the scene contains detail.

What matters now

Raw render quality is no longer the bottleneck. The active work is about interaction: making partial downloads useful, exposing uncertainty, and helping a person understand where a reconstruction is trustworthy. That is the difference between a paper implementation and a tool.