PANet: A Pixel-Level Attention Network for 6D Pose Estimation With Embedding Vector Features

Abstract

In this work, we present PANet, a pixel-level attention network with embedding vector features, which addresses the challenge of 6D pose estimation from a single RGBD image under severe occlusion. PANet produces pixel-wise attention for strong representation learning and leverages a novel selection scheme for robust pose estimation. Specifically, at the representation learning stage, we devise Pyramid Pixel-level Attention Module that unites attention mechanism with spatial pyramid to learn a discriminative representation, and Attention Upsample Module that utilizes arbitrary combinations of the CNN encoders’ feature maps to recover precise pixel-wise prediction, after which we embed the two modules into CNN to gain rich appearance features from RGB images. For depth images, we apply the current advanced point cloud network adopting attention mechanism to earn geometry features, which are further fused with the appearance features to obtain point-wise dense feature embedding. In the pose estimation stage, we define point-wise embedding vector features which can provide rich viewpoint information to better cope with the case of occluded objects. Further, a novel and effective RANSAC-based Selection Scheme is also founded to select vector features with high scores for pose estimation. Extensive experimental results manifest that our method outperforms the state-of-the-art by large margins on several benchmarks.