{
  "id": 42,
  "url": "https://arxiv.org/abs/2607.10461v1",
  "title": "Annotation-Free Furniture Codes: What They Encode, and How Far They Transfer",
  "summary": "Layout-based 3D scene synthesizers place each object using two human-annotated channels: a categorical class label and a canonical-pose convention. We ask whether a single self-supervised token derived from object geometry can replace both, and study such tokens directly as a representation, decoupled from any synthesizer. A Finite Scalar Quantization (FSQ) point-cloud autoencoder is chamfer-trained on placed 3D-FUTURE furniture with no labels or pose annotations. Diagnostic probes recover fine-",
  "authors": "Benjamin Friedman",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-11T19:54:52.000Z",
  "fetched_at": "2026-07-14T14:14:15.664Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/42",
  "original_url": "https://arxiv.org/abs/2607.10461v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}