SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation

Abstract

This paper presents an efficient symmetry-agnostic and correspondence-free framework, referred to as SC6D, for 6D object pose estimation from a single monocular RGB image. SC6D requires neither the 3D CAD model of the object nor any prior knowledge of the symmetries. The pose estimation is decomposed into three sub-tasks: a) object 3D rotation representation learning and matching; b) estimation of the 2D location of the object center; and c) scaleinvariant distance estimation (the translation along the zaxis) via classification. SC6D is evaluated on three benchmark datasets, T-LESS, YCB-V, and ITODD, and results in state-of-the-art performance on the T-LESS dataset. Moreover, SC6D is computationally much more efficient than the previous state-of-the-art method SurfEmb. The implementation and pre-trained models are publicly available at https://github.com/dingdingcai/SC6D-pose.

MSDA: Monocular Self-supervised Domain Adaptation for 6D Object Pose Estimation

“…Object-Specific Pose Estimation. Most existing pose estimation methods [2,3,5,12,16,18,24,37,44,49,52] are object-specific pose estimators, which are specialized for pre-defined objects and cannot generalize to previously unseen objects without retraining. Some of them [2,3,5,18,49,52] directly regress the 6D pose parameters from RGB images by training deep neural networks on a large number of labeled images.…”

“…Most existing pose estimation methods [2,3,5,12,16,18,24,37,44,49,52] are object-specific pose estimators, which are specialized for pre-defined objects and cannot generalize to previously unseen objects without retraining. Some of them [2,3,5,18,49,52] directly regress the 6D pose parameters from RGB images by training deep neural networks on a large number of labeled images. While other approaches [5,12,16,24,36,37,44] establish 2D-3D correspondences between 2D images and 3D object models to estimate the 6D pose by solving the Perspective-n-Point (PnP) [21] problem.…”

“…These keyframes are processed through DINOv2 and Co-Segmenter to jointly predict object segmentation masks. These predicted masks are then utilized to extract the object semantic tokens (F obj ) from the keyframe feature tokens (2)…”

“…We perform image segmentation for all reference images {I ref i } Nr i=1 using an Obj-Segmenter with the obtained semantic information F obj . Subsequently, we employ an RA-Encoder to extract the rotation-aware embeddings {V obj i } Nr i=1 from the segmented images (3)…”