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

TL;DR

SC6D introduces a symmetry-agnostic, correspondence-free framework for 6D object pose estimation from a single RGB image, achieving state-of-the-art results efficiently without requiring 3D models or symmetry prior knowledge.

Contribution

It proposes a novel, efficient approach that does not rely on object models or symmetry information, advancing 6D pose estimation methods.

Findings

State-of-the-art performance on T-LESS dataset.

More computationally efficient than previous methods.

Effective on multiple benchmark datasets.

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) scale-invariant distance estimation (the translation along the z-axis) 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.