BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation
This addresses the problem of accurate object pose estimation in cluttered environments for robotics and AR/VR applications, representing an incremental improvement over existing methods.
The paper tackles robust 6D pose estimation under severe occlusion and depth noise by proposing BiCo-Net, which combines global pose regression with local matching of oriented point pairs, achieving state-of-the-art performance on benchmark datasets, particularly in occluded scenes.
The challenges of learning a robust 6D pose function lie in 1) severe occlusion and 2) systematic noises in depth images. Inspired by the success of point-pair features, the goal of this paper is to recover the 6D pose of an object instance segmented from RGB-D images by locally matching pairs of oriented points between the model and camera space. To this end, we propose a novel Bi-directional Correspondence Mapping Network (BiCo-Net) to first generate point clouds guided by a typical pose regression, which can thus incorporate pose-sensitive information to optimize generation of local coordinates and their normal vectors. As pose predictions via geometric computation only rely on one single pair of local oriented points, our BiCo-Net can achieve robustness against sparse and occluded point clouds. An ensemble of redundant pose predictions from locally matching and direct pose regression further refines final pose output against noisy observations. Experimental results on three popularly benchmarking datasets can verify that our method can achieve state-of-the-art performance, especially for the more challenging severe occluded scenes. Source codes are available at https://github.com/Gorilla-Lab-SCUT/BiCo-Net.