SELF-SUPERVISED GEOMETRIC CORRESPONDENCE FOR CATEGORY-LEVEL 6D OBJECT POSE ESTIMATION IN THE WILD

Abstract

While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations. The problem becomes even more challenging when moving to category-level 6D pose, which requires generalization to unseen instances. Current approaches are restricted by leveraging annotations from simulation or collected from humans. In this paper, we overcome this barrier by introducing a self-supervised learning approach trained directly on large-scale real-world object videos for category-level 6D pose estimation in the wild. Our framework reconstructs the canonical 3D shape of an object category and learns dense correspondences between input images and the canonical shape via surface embedding. For training, we propose novel geometrical cycle-consistency losses which construct cycles across 2D-3D spaces, across different instances and different time steps. The learned correspondence can be applied for 6D pose estimation and other downstream tasks such as keypoint transfer. Surprisingly, our method, without any human annotations or simulators, can achieve on-par or even better performance than previous supervised or semisupervised methods on in-the-wild images. Code and videos are available at https://kywind.github.io/self-pose.

1. INTRODUCTION

Object 6D pose estimation is a long-standing problem for computer vision and robotics. In instancelevel 6D pose estimation, a model is trained to estimate the 6D pose for one single instance given its 3D shape template (He et al., 2020; Xiang et al., 2017; Oberweger et al., 2018) . For generalizing to unseen objects and removing the requirement of 3D CAD templates, approaches for category-level 6D pose estimation are proposed (Wang et al., 2019b) . However, learning a generalizable model requires a large amount of data and supervision. A common solution in most approaches (Wang et al., 2019b; Tian et al., 2020; Chen et al., 2020a; 2021; Lin et al., 2021) is leveraging both real-world (Wang et al., 2019b) and simulation labels (Wang et al., 2019b; Chang et al., 2015) at the same time for training. While there are limited labels from the real world given the high cost of 3D annotations, we can generate as many annotations as we want in simulation for free. However, it is very hard to model the large diversity of in-the-wild objects with a simulator, which introduces a large sim-to-real gap when transferring the model trained with synthetic data. Although the real-world labels are hard to obtain, the large-scale object data is much more achievable (Fu & Wang, 2022) . In this paper, we propose a self-supervised learning approach that directly trains on large-scale unlabeled object-centric videos for category-level 6D pose estimation. Our method does not require any 6D pose annotations from simulation or human labor for learning. This allows the trained model to generalize to in-the-wild data. Given a 3D object shape prior for each category, our model learns the 2D-3D dense correspondences between the input image pixels and the 3D points on the categorical shape prior, namely geometric correspondence. The object 6D pose can be solved with the correspondence pairs and the depth map using a pose fitting algorithm. We propose a novel Categorical Surface Embedding (CSE) representation, which is a feature field defined over the surface of the categorical canonical object mesh. Every vertex of the canonical mesh is encoded into a feature embedding to form the CSE. Given an input image, we use an image encoder to extract the pixel features to the same embedding space. By computing the similarity † Work done while an intern at UC San Diego. 1

