arXiv (All)
open access
eVGGT: An Efficient Geometry-Aware Vision Encoder for Visuomotor Policies
Geometry-grounded vision models, such as VGGT, have emerged as robust visual encoders, providing essential geometric priors for robotic manipulation. However, the high computational cost of these models often leads to slow inference, limiting their practical applications in real-world robotics. This
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
- V2-STRep: VLM-Grounded Structured Task Representations for Reusable Robot Skills Acquired from Generated Videos#1034394
- Learning Where Outcomes Change:Credit-Addressable Reasoning for Multimodal Geometry#799658
- Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation#1143831
- SFE-VGGT: Source-Free VGGT Distillation for Event-Based Monocular Depth Estimation#1132130
- vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models#1049867
Record · ID 1118419
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