arXiv (All)
open access
Align-VAE: Aligning Attention and Geometry in VecSet VAEs for 3D Reconstruction
Set-based variational autoencoders (VecSet VAEs) represent 3D shapes as compact sets of latent tokens. Although each token originates from a query at a sampled surface location, whether these latents truly align with geometry remains unclear. We investigate this alignment and identify four issues: (
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- CoVA: Composing VAE Representations for Generative Learning#1297674
- NeuSOGA3D: A Neuro-Symbolic Framework for Explainable 3D Geometric Reconstruction#1035656
- NeuSOGA3D: A Neuro-Symbolic Framework for Explainable 3D Geometric Reconstruction#978487
- VGGT-Align: Bridging Local Reconstruction and Global Consistency for Long-Sequence 3D Reconstruction#615238
- VGGT-Align: Bridging Local Reconstruction and Global Consistency for Long-Sequence 3D Reconstruction#614271
Record · ID 1325630
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive