arXiv (All)
open access
BAT3R: Robust Online 3D Reconstruction with Bayesian Adaptive State Updates
Feed-forward transformer models have driven remarkable progress in 3D vision,yet their quadratic complexity renders them impractical for long video sequences. Streaming 3D reconstruction addresses this bottleneck by processing frames sequen?tially with constant memory. However, existing recurrent ar
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
- FILT3R: Latent State Adaptive Kalman Filter for Streaming 3D Reconstruction#973094
- Slot3R: Set-Associative Spatial Memory for Streaming 3D Reconstruction#1249706
- RIGOR: Rig-Informed Geometry for Omnidirectional Reconstruction#1071288
- Motion- and Gravity-Aware Feedforward 3D Reconstruction#1357832
- Motion- and Gravity-Aware Feedforward 3D Reconstruction#1348903
Record · ID 1327920
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