arXiv (All)
open access
VGGT-Bridge: Beyond Sequential Pose Graphs via Coarse-Stride Skip Edges
Feed-forward visual geometry transformers such as VGGT reconstruct dense 3D structure from images in a single forward pass, simplifying multi-view 3D reconstruction. However, their quadratic attention complexity makes them difficult to scale to long sequences with thousands of frames. Chunk-and-alig
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
- Think Locally, Refine Globally for Memory-Efficient 3D Reconstruction#1035841
- 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
- TurboVGGT: Fast Visual Geometry Reconstruction with Adaptive Alternating Attention#1344579
- ScenePlacer: Reformulating Scene-Level Object Pose Estimation as Visibility Field Learning#1326984
Record · ID 1215691
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