# 3D Neural Coding — where the determinism contract goes next

> Landscape note, not a normative extension. Learned coding is moving from flat
> media into 3D — point clouds and Gaussian splats — and it carries the same
> cross-platform non-determinism that flat learned codecs have. WAI already
> decodes one such 3D representation byte-exactly (`wai.splat.int_codec`); this
> records how the determinism contract and the reconstruction binding generalize
> to the emerging 3D coding standards, and where WAI does and does not connect to
> them. Framed self-referentially — the standards are named as standards, no
> comparisons.

## The landscape

The learned image-codec machinery is being reused one dimension up:

- **JPEG Pleno Learning-based Point Cloud Coding** is finalized, and it **reuses
  JPEG AI** to code the point cloud's colour attributes — the same learned-2D
  transform, applied to attribute images.
- **Gaussian Splat Coding** is under standardization (MPEG WG working groups,
  JPEG), applying **Neural Network Coding (NNC)** to the splat parameters —
  spherical-harmonic coefficients, positions, scales.

Both inherit the property that made the flat learned codecs hard to deploy: a
learned decode is not reproducible across hardware. A float attribute/coefficient
decode drifts by round-off, the entropy stage desyncs, and — exactly as for 2D —
the reconstruction is reproducible only on the same build. The determinism
problem, and its fix, are the same shape in 3D.

## What WAI already has

`wai.splat.int_codec` is a byte-exact learned Gaussian-splat **attribute** codec:
a learned transform over the `[14, S, S]` attribute image (position, scale,
rotation, colour, opacity), integer synthesis over a factorized-rANS latent, pure
`i64` — no float, no ONNX. Its conformance criterion is **splat-equivalence**: the
decoded attribute tensor is byte-identical on every machine, a portable cloud
hash. The float rasterizer stays sink-supplied presentation; the *attributes* are
the contract.

That is the 3D instance of the same thesis WAI applies to `wai.image.jpegai`: the
learned decode to the medium's canonical representation is pinned and integer, so
the reconstruction has a portable hash. `wai.splat.int_codec` already sits at the
**`decode-equivalence`** tier (see [`extensions/determinism-tiers.md`]) — the
attribute tensor is the "final medium" whose bytes reproduce.

## Where it connects — and where it doesn't

- **The contract generalizes.** `decode-equivalence` + a
  [reproducible-reconstruction binding](../extensions/reconstruction-binding.md)
  over a decoded 3D representation is exactly the same construction as over decoded
  pixels: hash the canonical decoded buffer (here the attribute tensor, or a point
  cloud's decoded attributes), pin the descriptor, and the rendered-content
  provenance binding neural-coded media could not carry becomes available for 3D
  too. As the 3D standards adopt learned coding, the determinism-for-provenance gap
  is the same one WAI closes for 2D.
- **Honest scope — WAI does not decode the ISO point-cloud bitstream.**
  `wai.splat.int_codec` is a WAI-native integer codec, not the JPEG-Pleno PCC or
  the MPEG splat-coding stream. The shared thing is the *problem and the approach*
  (a learned 3D decode that needs to be reproducible), not the wire format. A
  future `wai.pointcloud.*` that byte-exactly decodes a published 3D standard's
  codestream would be the 3D analogue of what `wai.image.jpegai` is for JPEG AI —
  and, because JPEG-Pleno PCC reuses JPEG AI for its colour attributes, the
  byte-exact JPEG-AI decode WAI already has is a component that path could build
  on. Not built; recorded as the natural next capability if the 3D neural-coding
  standards land in scope.

## Takeaway

The determinism contract is not image-specific. WAI's byte-exact family already
spans image (`jpegai`, `int_hyper`), audio (`int_codec`), and 3D
(`splat.int_codec`); the reconstruction binding and the tier vocabulary apply to
all of them. The emerging 3D neural-coding standards face the same
non-determinism WAI addresses for 2D, and the JPEG-AI decode WAI has is a
component of the point-cloud path — so the ingest is: keep the determinism
contract media-general, and treat a byte-exact decode of a published 3D standard
as the natural next capability, not a separate track.