LATO.2 hardcodes reduce='amax' but Pixal3D's version of the same op defaults to
'mean'. Both now share one implementation, so relying on its default would silently
change every downsampled feature if that default ever moved.
Pixal3D is built on the same TRELLIS.2 sparse module, so the core belongs in one
tested package rather than a copy per port. Verified behaviour-preserving: the
encoder still loads 102/102 params and produces a bit-identical latent
(mean +0.0508, std 0.9186) after the extraction.
102/102 encoder params load with 0 missing and 0 unmapped. A synthetic voxelised
sphere shell (16,934 voxels) encodes to 56 latent voxels in 135ms on m3ultra, and the
latent comes out mean +0.05 / std 0.92 - the approximately unit-normal distribution a
KL-trained VAE should produce, which is decent evidence the graph and the sparse conv
path are right.
Architecture is inferred from tensor shapes, not constructor defaults: upstream
defaults latent_dim to 8 but the released weights say 32, and attn_mode/pe_mode
defaults are likewise overridden by the trained config. infer_config() reads it off
the checkpoint.
Also added SparseDownsample. Upstream's docstring says average pooling but the code
passes reduce='amax' - following the code.
Kernel orientation: tried latent statistics as a cheap discriminator and it does NOT
work. The flip is not a no-op (max delta 3.53) but both orientations give a plausible
near-unit-normal latent (std 0.919 vs 0.945). Recorded as a negative result; it needs
the decoder and reconstruction quality to settle.
