# 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 reported to reuse JPEG AI to
  code the point cloud's colour attributes — the same learned 2-D transform,
  applied to attribute images. *(Not verified here: the tree records no source for
  this, and none was read for this note's 2026-09-30 revision.)*
- **Gaussian splat coding** is being standardized in MPEG along two paths, per
  MPEG's pages and public output documents read on 2026-09-30 (listed in
  [`media-codec-registry.md`](media-codec-registry.md), *MPEG's Gaussian-splat and
  point-cloud work*): amendments that add Gaussian splats to the two point-cloud
  standards, V-PCC (ISO/IEC 23090-5, at Draft Amendment) and G-PCC (ISO/IEC
  23090-9, at Committee Draft Amendment); and a joint exploration (Exploration 45),
  coordinated across WG 4, WG 5/JVET and WG 7 of ISO/IEC JTC 1/SC 29, that is
  preparing a call for proposals for content in the I-3DGS representation.
  Separately, a Committee Draft amendment of **Neural Network Coding (NNC)**
  extends NNC to "parameters of 3D and 4D scene representations such as Gaussian
  splats" ([2030 landscape](2030-transport-landscape.md) §6.4). No source read here
  establishes a JPEG activity on splat coding.
- **AI-based point cloud coding** is MPEG-AI Part 5 (ISO/IEC 23888-5); its part
  page lists common test conditions at MPEG 155
  ([2026-09 sweep](2026-09-global-sweep.md) §2.1).

Where these paths use a learned decode, they inherit the property that made the
flat learned codecs hard to deploy: a float decode drifts by round-off, the
entropy stage desyncs, and the reconstruction is reproducible only on the same
build. Whether a given path's decode is learned is not established here: the
amendment texts are not public, and the call for proposals has selected no
technology. The V-PCC amendment specifies a reconstruction profile because
"without a clearly specified reconstruction profile, different implementations
could decode the same bitstream into different Gaussian Splat parameters"
(ISO/IEC JTC 1/SC 29/WG 4 N 837).

## 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 its integer image
codecs (`wai.neural.int_hyper`): 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`](../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.
- **Scope — WAI does not decode the ISO/IEC point-cloud bitstreams.**
  `wai.splat.int_codec` is a WAI-native integer codec, not a JPEG Pleno or MPEG
  bitstream; the shared thing is the *problem and the approach*, not the wire
  format. WAI already registers MPEG's point-cloud standards as sink-supplied
  capabilities — `wai.pc.gpcc` (G-PCC, ISO/IEC 23090-9) and `wai.pc.vpcc` (V-PCC,
  ISO/IEC 23090-5), replicate class `StandardUnclassified`, with no decoder in
  `wai-rs` — and the research registry records a sink-side G-PCC decode and a V-PCC
  encode with a structural V3C parse through the MPEG reference tools. A decoder in
  WAI that reproduces a registered standard's reconstruction byte for byte would
  decode that capability's existing payload, as WAI's exact JPEG AI decoder decodes
  `wai.image.jpegai`; it adds no capability string. A 3D standard WAI does not
  register yet would need a string of its own (SPEC §5, §8); which editions and
  amendments the two `wai.pc.*` strings carry follows SPEC §8's edition rule, and
  what remains open is recorded in [`media-codec-registry.md`](media-codec-registry.md),
  open item (i). If JPEG Pleno's learning-based coding reuses JPEG AI for colour
  attributes (unverified, above), WAI's JPEG AI decoder — integer entropy decode
  and synthesis networks, with `wai.image.jpegai` registered at
  `entropy-consistency` because its later stages are float — is a component that
  path could build on. Not built.

## Takeaway

The determinism contract is not image-specific. WAI's byte-exact family — the
registry's `NeuralIntegerExact` class (`codecs::capability_replicate_class`) —
already spans image, video, audio and 3D, with `jpegai` at `entropy-consistency`
beside it; the reconstruction binding and the tier vocabulary apply to all of
them. The emerging 3D coding standards that adopt a learned decode face the same
non-determinism WAI addresses for 2D, 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.