0000−0003−0587−9643] , Zoltan-Csaba Marton 1[0000−0002−3035−493X] , Maximilian Durner 1[0000−0001−8885−5334] , Manuel Brucker 1[0000−0001−6370−2753] , Rudolph Triebel 1,2[0000−0002−7975−036X] Abstract. We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries. Instead of learning an explicit mapping from input images to object poses, it provides an implicit representation of object orientations defined by samples in a latent space. Experiments on the T-LESS and LineMOD datasets show that our method outperforms similar modelbased approaches and competes with state-of-the art approaches that require real pose-annotated images. 1
Recently, REF proposed a selfsupervised Augmented Autoencoder that trains on CAD models, removing the need for pose-annotated training data.
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
European Conference on Computer Vision (ECCV), 2018
Computer Science