arXiv (All)
open access
ControlFlow3D: Distilling Multi-View Knowledge into Latent Flow Matching for Point Cloud Upsampling
Point cloud upsampling is essential for 3D applications but remains challenging due to sparse, under-constrained observations. Multi-view images can resolve geometric ambiguity, but existing multi-modal methods do not fully exploit visual cues. We propose ControlFlow3D, a framework that distills kno
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
- FlowVLA: Enabling World Dynamics Understanding through Pluggable 3D Flow Learning#1327750
- Less Decoder is More Encoder: Geometric Representation Learning from Novel View Synthesis#1188428
- Less Decoder is More Encoder: Geometric Representation Learning from Novel View Synthesis#1181666
- ReconSplat: Generalizable 3D Scene Reconstruction Beyond Observed Views#793128
- ReconSplat: Generalizable 3D Scene Reconstruction Beyond Observed Views#789124
Record · ID 1356526
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