pith:L5R24ISC
MiXR: Harvesting and Recomposing Geometry from Real-World Objects for In-Situ 3D Design
MiXR lets users harvest real-world geometry segments in XR and assemble them by direct manipulation before generative AI synthesizes the final model.
arxiv:2605.09620 v2 · 2026-05-10 · cs.HC
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\pithnumber{L5R24ISCP7IBMOQA7IM5JMIPIK}
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Claims
In a controlled user study (N=12), participants using MiXR rated their designs as significantly closer to the target, felt more in control, and experienced lower cognitive workload compared to a generative composition baseline.
That generative AI can reliably synthesize coherent, artifact-free 3D models from arbitrary user-specified spatial compositions extracted from real-world captures.
MiXR enables in-situ 3D design by harvesting real-world geometry for user-defined compositions that generative AI then refines, outperforming text-only generative methods in control and fidelity per a 12-person study.
Receipt and verification
| First computed | 2026-05-21T01:04:27.370881Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5f63ae22427fd0163a00fa19d4b10f428987b250fc4b6a53f6faa8a96deaa044
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/L5R24ISCP7IBMOQA7IM5JMIPIK \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5f63ae22427fd0163a00fa19d4b10f428987b250fc4b6a53f6faa8a96deaa044
Canonical record JSON
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