arXiv — cs.AI preprintsInternational5 October 2026
OmniAct3D: Leveraging Foundation Geometry and Evidence-Grounded Reasoning for Panoramic 3D Detection
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.03015v1 Announce Type: cross Abstract: Accurate 3D detection is essential for mobile embodied agents, while Vision Foundation Models (VFMs) offer transferable visual and geometric priors. Yet existing VFM-based 3D detectors rely on narrow-view monocular images or discrete perspective views, limiting coherent surround perception; equirectangular projection (ERP) instead encodes a continuous 360 scene in a single image. Direct transfer remains difficult because ERP organizes geometry and visual information differently, making object-relevant cues hard to model, localize, and preserve.
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- MintFlow: Minimal Trajectory Intervention for Constrained Flow Matching5 October 2026
- Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses5 October 2026
- The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?5 October 2026
- Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation5 October 2026
- Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents5 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.