Multi-Layer Feature Exchange Transformer for Multi-View 6D Object Pose Estimation in Robot Bin Picking

Abstract

Accurate 6D object pose estimation is crucial in industrial automation, particularly in robotic bin picking, where objects are often textureless, reflective, and arranged in cluttered environments. Multi-view pose estimation methods offer significant advantages over single-view methods by providing more comprehensive information, effectively handling occlusions and lack of features, and resolving depth ambiguities. However, current multi-view methods often rely on late-stage information fusion, limiting their ability to fully exploit complementary multi-view data. This paper presents a novel approach to enhance multiview 6D pose estimation by introducing a Feature Exchange Transformer (FET) for early-stage feature fusion. This approach leverages self-attention and epipolar cross-attention mechanisms to enable multi-layer feature aggregation across views. Additionally, we introduce a coarse-to-fine strategy for an efficient feature exchange at multiple network layers. Our method, implemented on top of EpiSurfEmb[1], enhances the utilization of multi-view information, leading to significant improvements in pose estimation accuracy and robustness, especially in challenging bin-picking scenarios. We evaluate our approach on the ROBI dataset, demonstrating that it outperforms both the baseline EpiSurfEmb and other state-of-the-art multi-view pose estimation methods.