  Estimating the 6D pose of objects using only RGB images remains challenging
because of problems such as occlusion and symmetries. It is also difficult to
construct 3D models with precise texture without expert knowledge or
specialized scanning devices. To address these problems, we propose a novel
pose estimation method, Pix2Pose, that predicts the 3D coordinates of each
object pixel without textured models. An auto-encoder architecture is designed
to estimate the 3D coordinates and expected errors per pixel. These pixel-wise
predictions are then used in multiple stages to form 2D-3D correspondences to
directly compute poses with the PnP algorithm with RANSAC iterations. Our
method is robust to occlusion by leveraging recent achievements in generative
adversarial training to precisely recover occluded parts. Furthermore, a novel
loss function, the transformer loss, is proposed to handle symmetric objects by
guiding predictions to the closest symmetric pose. Evaluations on three
different benchmark datasets containing symmetric and occluded objects show our
method outperforms the state of the art using only RGB images.
