Neural Harmonics Texture integration#235
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…ecialization `HitParticleT<HasCanonical>` splits the k-buffer hit record into an SH base and an NHT derived that adds the `canonicalIntersection` float3. The renderer aliases pick the right one from `Params::PerRayParticleFeatures`, so the SH path pays no storage for a field it never reads. `densityHit` routes its out-arg through `canonicalIntersectionSlot` (struct field or stack scratch). The forward feature ternary becomes `if constexpr` so the SH specialization does not name-lookup a missing member. Made-with: Cursor
Updates NHT encoding, tetrahedral interpolation, alpha handling, initialization, and refinement behavior so benchmark runs are closer to the GSplat reference while preserving SH defaults.
Keeps the production NHT backward path unconditional so diagnostic-only broken modes cannot affect training builds.
Aligns final evaluation with EMA semantics, keeps resumed training on live decoder weights, and mirrors forward hit bounds in the 3DGUT no-k-buffer backward path.
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Adds Neural Harmonic Textures (NHT) as a new per-particle feature model for both 3DGRT (OptiX/Slang) and 3DGUT (CUDA/Slang), alongside the existing SH radiance path.
Generalizes the rendering pipeline from fixed 3-channel radiance to arbitrary-dim particle/ray features, with an optional MLP feature decoder (threedgrut/model/feature_decoder.py, features.py).
Adds optional FP16 storage for per-particle features (render.particle_feature_half) and for the integrated per-ray feature output (render.feature_output_half); gradients stay FP32.
New app configs: {nerf_synthetic,colmap}_{3dgrt,3dgut}_mcmc_nht.yaml.