COLMAR: Cooperative View Policy Learning for Multi-Agent Active 3D Reconstruction

Jul 16, 2026

COLMAR is a cooperative view policy learning framework designed for multi-agent active 3D reconstruction. It introduces shared policy optimization and reconstruction-aware objectives to improve coordination among agents, reducing redundant observations and enhancing coverage. Experiments on the GLEAM and Replica datasets show that COLMAR achieves up to 54% higher reconstruction accuracy and 49% greater coverage compared to baseline methods.

Why it matters: Improving coordination in multi-agent 3D reconstruction can significantly enhance the efficiency and quality of autonomous exploration and mapping systems.

Full story at: arXiv Robotics ↗