Hybrid 6D Object Pose Estimation from the RGB Image

Open Webpage

Rafal Staszak, Dominik Belter

Published: 01 Jan 2019, Last Modified: 04 Nov 2025ICINCO (1) 2019EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: In this research, we focus on the 6D pose estimation of known objects from the RGB image. In contrast to state of the art methods, which are based on the end-to-end neural network training, we proposed a hybrid approach. We use separate deep neural networks to: detect the object on the image, estimate the center of the object, and estimate the translation and ”in-place” rotation of the object. Then, we use geometrical relations on the image and the camera model to recover the full 6D object pose. As a result, we avoid the direct estimation of the object orientation defined in SO3 using a neural network. We propose the 4D-NET neural network to estimate translation and ”in-place” rotation of the object. Finally, we show results on the images generated from the Pascal VOC and ShapeNet datasets.