MRPose: Multi‐Robot Relative 6D Pose Estimation for Unseen Objects From RGB Images

Abstract

Relative pose estimation under the query–reference paradigm has emerged as a practical alternative for estimating the 6D pose of unseen objects without relying on CAD models or extensive annotations. However, many existing approaches remain difficult to deploy in practice, as their reliance on geometric matching makes robustness highly sensitive to matching quality. In this paper, we present MRPose, a framework for relative 6D object pose estimation under a multi‐robot setting, where query and reference views are independently captured by different robots. Without requiring CAD models, shared calibration across robots or pose annotations, MRPose directly estimates the full relative transformation between unseen objects from RGB images. Built upon the power of vision foundation models, MRPose introduces a multiplex feature learning strategy that jointly exploits patch‐level appearance correspondence and sparse geometric cues through attention‐based cross‐view feature aggregation. Instead of relying on traditional geometric solvers such as PnP or essential matrix estimation, the proposed framework directly regresses the full relative 6D pose in an end‐to‐end manner. Extensive experiments on the LINEMOD and YCB‐Video benchmarks demonstrate that MRPose compares favourably with existing RGB‐based relative pose estimation methods. Real‐world experiments further validate its applicability in practical scenarios.