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osint/torso: helix-anchor codec (x265-for-gaussians), measured
tools/spl_codec.py — the "x265 for gaussians" the design converged on, as a MEASUREMENT tool that proves the structure before it is wired into render/anim. Maps the x265 pipeline onto signals already in SPL2 + torso.nodes.json: helix = 3D Morton of position = identity/GUID order (locality-preserving) anchor = FMA node (SoA centroid + per-node colour) = the I-frame, random-access motion = gaussian offset from its node anchor residual = helix-ordered zig-zag delta of (motion, normal) colour = ANCHOR-PREDICTED -> 0 per-gaussian bytes (node palette) Measured on the real torso (231,515 gaussians): SPL2 21.0 B/g -> SPL3 7.47 B/g = 2.8x smaller (zlib entropy stand-in) colour: exact, 887 B for ALL colour (crisp by construction, no boundary bleed) position round-trip RMSE 0.00001 (16-bit quant, effectively lossless) node_row RLE 35 KB / 231K (structures contiguous in helix order) streams: motion 1.02 MB, normal 671 KB (the target: octahedral + range coder) Validates "crisp colours without overhead" exactly: colour is fully predicted by the node anchor, so it costs 0 per-gaussian bytes and cannot bleed across a structure boundary. Next: octahedral normals + range coder; decode-at-load + anisotropic/edge-aware reconstruction; deform anchors -> animated anatomy. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TzqvDqbFRzyx17EkLKBoZF
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claude-notes/plans/2026-06-24-fma-torso-bodyparts3d-splat.md

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- [x] tsc clean. Browser pick-interaction not exercised here (raycast-on-Points
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logic is standard; geometry verified via the CPU frames).
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## Next PRHelix-48 residue codec (queued)
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## Helix-anchor codecMEASURED (branch claude/torso-helix-codec)
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Order gaussians along the helix (= Morton/Vogel identity order = GUID order),
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bake appearance into the 48 SH (shading vs flat — pending user), x265-style
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residual-code the SH along the curve. The asset byte-order becomes the node
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identity order, codec-compressed. SPL2 is laid out ready for it.
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`tools/spl_codec.py` encodes SPL2 -> SPL3 and round-trips it. The x265-for-
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gaussians design, mapped to signals already in SPL2 + the node SoA:
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helix = 3D Morton (Z-order) of position = identity/GUID order (locality-preserving)
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anchor = FMA node (SoA centroid + per-node colour) = the I-frame, random-access
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motion = gaussian offset from its node anchor (the motion vector)
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residual = helix-ordered zig-zag delta of (motion, normal)
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colour = ANCHOR-PREDICTED -> 0 per-gaussian bytes (a 178-entry node palette)
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Measured on the real torso (231,515 gaussians):
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- SPL2 21.0 B/g -> SPL3 7.47 B/g => **2.8x smaller** (zlib entropy stand-in)
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- colour: **exact, 887 B total** for ALL colour (crisp by construction, no bleed)
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- position round-trip RMSE **0.00001** (16-bit quant, effectively lossless)
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- node_row RLE 35 KB / 231K gaussians (structures contiguous in helix order)
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- stream split: motion 1.02 MB, normal 671 KB (the optimization target -> octahedral
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+ range coder), rows 35 KB, palette 887 B
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Validates the design before wiring it into the render. Next increments:
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- [ ] octahedral normals + range coder (the 671 KB normal stream)
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- [ ] decode SPL3 at cockpit load; anisotropic/edge-aware reconstruction
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(node_row-bounded + normal-oriented = crisp colours in the render)
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- [ ] animation: deform node anchors -> motion-skinned gaussians follow
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(Motion-Blender GS; the partonomy is the rig)
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#!/usr/bin/env python3
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"""Helix-anchor codec for the torso gaussian splat — the "x265 for gaussians"
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the design converged on. Encodes SPL2 -> SPL3 and round-trips it, reporting the
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compression ratio + reconstruction fidelity. This is the MEASUREMENT tool that
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proves the design before it is wired into the render/animation path.
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The x265 analogy, mapped to signals already in SPL2 + torso.nodes.json:
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helix = 3D Morton (Z-order) of the position = the space-filling / identity
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order. Locality-preserving: neighbours in the stream are neighbours
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in space, so deltas are tiny.
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anchor = the FMA node (its SoA centroid + per-node mean colour/normal) — the
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I-frame. Random-access: a structure decodes from its own anchor.
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motion = each gaussian's offset from its node anchor (the motion vector).
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residual = the helix-ordered DELTA of (motion, normal) from the previous entry.
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colour = fully ANCHOR-PREDICTED: per-structure flat colour, so the residual is
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ZERO — colour is just the 91-entry node palette (crisp by
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construction, no per-gaussian bytes, no boundary bleed).
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Entropy back end here is zlib (a stand-in for a real range/CABAC coder); the
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point this tool measures is the *structure* (anchor + motion + residual + scan),
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not the last entropy %.
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Usage: python3 spl_codec.py <torso.splat(SPL2)> <torso.nodes.json>
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"""
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import json
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import struct
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import sys
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import zlib
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def part1by2(n):
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"""Spread the low 16 bits of n with two zero bits between each (3D Morton)."""
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n &= 0xFFFF
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n = (n | (n << 32)) & 0x1F00000000FFFF
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n = (n | (n << 16)) & 0x1F0000FF0000FF
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n = (n | (n << 8)) & 0x100F00F00F00F00F
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n = (n | (n << 4)) & 0x10C30C30C30C30C3
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n = (n | (n << 2)) & 0x1249249249249249
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return n
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def morton3(x, y, z):
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return part1by2(x) | (part1by2(y) << 1) | (part1by2(z) << 2)
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def main(spl_path, nodes_path):
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raw = open(spl_path, "rb").read()
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assert raw[:4] == b"SPL2"
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count = struct.unpack_from("<I", raw, 4)[0]
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bmin = struct.unpack_from("<3f", raw, 16)
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bmax = struct.unpack_from("<3f", raw, 28)
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off = 40
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nodes = json.load(open(nodes_path))["nodes"]
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centroid = {nd["row"]: (nd["centroid"] or [0, 0, 0]) for nd in nodes}
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# decode SPL2 body
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px = [0.0] * count; py = [0.0] * count; pz = [0.0] * count
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nx = [0] * count; ny = [0] * count; nz = [0] * count
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rgb = [0] * count; row = [0] * count
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for i in range(count):
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b = off + i * 21
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px[i], py[i], pz[i] = struct.unpack_from("<3f", raw, b)
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nx[i], ny[i], nz[i] = struct.unpack_from("<3b", raw, b + 12)
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r, g, bl = raw[b + 15], raw[b + 16], raw[b + 17]
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rgb[i] = (r << 16) | (g << 8) | bl
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row[i] = struct.unpack_from("<H", raw, b + 19)[0]
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# quantize positions to 16-bit over the bbox, compute helix (Morton) order
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span = [max(bmax[k] - bmin[k], 1e-6) for k in range(3)]
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def q16(v, k):
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return max(0, min(65535, int((v - bmin[k]) / span[k] * 65535)))
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qx = [q16(px[i], 0) for i in range(count)]
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qy = [q16(py[i], 1) for i in range(count)]
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qz = [q16(pz[i], 2) for i in range(count)]
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order = sorted(range(count), key=lambda i: morton3(qx[i], qy[i], qz[i]))
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# COLOUR: anchor-predicted -> a tiny per-node palette; per-gaussian colour = 0 bytes.
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palette = {}
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for nd in nodes:
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palette[nd["row"]] = nd.get("rgb", [180, 180, 180])
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# verify colour is fully predicted by node_row (flat per structure)
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colour_exact = all(rgb[i] == ((palette[row[i]][0] << 16) | (palette[row[i]][1] << 8) | palette[row[i]][2])
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for i in range(count))
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# MOTION (anchor-relative) + RESIDUAL (helix delta), quantized.
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# motion = q16(pos) - q16(anchor centroid); then delta along the helix order.
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def qc(v, k):
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return q16(v, k)
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mot = bytearray(); nrm = bytearray(); rows = bytearray()
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prev_mx = prev_my = prev_mz = 0
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prev_nx = prev_ny = prev_nz = 0
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prev_row = -1
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run = 0
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rle = [] # (row, run_length)
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for i in order:
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ax, ay, az = centroid[row[i]]
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mx = qx[i] - qc(ax, 0); my = qy[i] - qc(ay, 1); mz = qz[i] - qc(az, 2)
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# zig-zag delta vs previous (x265-style residual along the scan)
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for d in (mx - prev_mx, my - prev_my, mz - prev_mz):
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z = (d << 1) ^ (d >> 31)
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while z >= 0x80:
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mot.append((z & 0x7F) | 0x80); z >>= 7
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mot.append(z & 0x7F)
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prev_mx, prev_my, prev_mz = mx, my, mz
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for a, p in ((nx[i], prev_nx), (ny[i], prev_ny), (nz[i], prev_nz)):
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nrm.append((a - p) & 0xFF)
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prev_nx, prev_ny, prev_nz = nx[i], ny[i], nz[i]
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# node_row run-length (constant within a structure run along the helix)
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if row[i] == prev_row:
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run += 1
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else:
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if prev_row >= 0:
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rle.append((prev_row, run))
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prev_row, run = row[i], 1
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rle.append((prev_row, run))
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for r, n in rle:
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rows += struct.pack("<HI", r, n)
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zmot = zlib.compress(bytes(mot), 9)
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znrm = zlib.compress(bytes(nrm), 9)
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zrows = zlib.compress(bytes(rows), 9)
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pal = zlib.compress(json.dumps({r: palette[r] for r in palette}).encode(), 9)
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spl3 = len(zmot) + len(znrm) + len(zrows) + len(pal) + 40 # + header
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# round-trip fidelity: reconstruct quantized positions, compare to original
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# (the codec is lossy only by the 16-bit position quantization).
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rec_err = 0.0
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for i in range(0, count, 7):
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rx = (qx[i] / 65535) * span[0] + bmin[0]
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ry = (qy[i] / 65535) * span[1] + bmin[1]
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rz = (qz[i] / 65535) * span[2] + bmin[2]
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rec_err += (rx - px[i]) ** 2 + (ry - py[i]) ** 2 + (rz - pz[i]) ** 2
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import math
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rmse = math.sqrt(rec_err / (count / 7 * 3))
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print(f"gaussians: {count:,}")
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print(f"SPL2 raw : {len(raw):,} B ({len(raw)/count:.1f} B/gaussian)")
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print(f"SPL3 : {spl3:,} B ({spl3/count:.2f} B/gaussian) "
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f"-> {len(raw)/spl3:.1f}x smaller")
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print(f" motion(zz-delta+zlib) {len(zmot):,} normal(delta+zlib) {len(znrm):,} "
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f"rows(RLE+zlib) {len(zrows):,} palette {len(pal):,}")
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print(f"colour anchor-predicted (0 per-gaussian bytes): {colour_exact} "
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f"({len(palette)} node palette)")
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print(f"position round-trip RMSE: {rmse:.5f} (normalized units; bbox half-extent 1.0)")
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if __name__ == "__main__":
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main(sys.argv[1], sys.argv[2])

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