Self-Supervised Object Pose Estimation With Multi-Task Learning

Object pose estimation using learning-based methods often necessitates vast amounts of meticulously labeled training data. The process of capturing real-world object images under diverse conditions and annotating these images with 6 Degrees of Freedom (6DOF) object poses is both time-consuming and resource-intensive. In this study, we propose an innovative approach to monocular 6D pose estimation through self-supervised learning, eliminating the need for labor-intensive manual annotations. Our method initiates by training a multi-task neural network in a fully supervised manner, leveraging synthetic RGBD data. We leverage semantic segmentation, instance-level depth estimation, and vector-field prediction as auxiliary tasks to enhance the primary task of pose estimation. Subsequently, we harness advancements in multi-task learning to further self-supervise the model using unlabeled real-world RGB data. A pivotal element of our self-supervised object pose estimation is a geometry-guided pseudo-label filtering module that relies on estimated depth from instance-level depth estimation. Our extensive experiments conducted on benchmark datasets demonstrate the effectiveness and potential of our approach in achieving accurate monocular 6D pose estimation. Importantly, our method showcases a promising avenue for overcoming the challenges associated with the labor-intensive annotation process, offering a more efficient and scalable solution for real-world object pose estimation.