ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework

Abstract

In this paper, a computation efficient regression framework is presented for estimating the 6D pose of rigid objects from a single RGB-D image, which is applicable to handling symmetric objects. This framework is designed in a simple architecture that efficiently extracts point-wise features from RGB-D data using a fully convolutional network, called XYZNet, and directly regresses the 6D pose without any post refinement. In the case of symmetric object, one object has multiple ground-truth poses, and this one-to-many relationship may lead to estimation ambiguity. In order to solve this ambiguity problem, we design a symmetry-invariant pose distance metric, called average (maximum) grouped primitives distance or A(M)GPD. The proposed A(M)GPD loss can make the regression network converge to the correct state, i.e., all minima in the A(M)GPD loss surface are mapped to the correct poses. Extensive experiments on YCB-Video and T-LESS datasets demonstrate the proposed framework's substantially superior performance in top accuracy and low computational cost. The relevant code is available in https://github.com/GANWANSHUI/ES6D.git.

AG-Net: Category-level 6D Pose Estimation Network Based on Attention Mechanism and Global Enhancement

“…6D pose estimation plays a crucial role in various fields such as augmented reality [1], robot grasping [2], and autonomous driving [3]. In recent years, many instance-level pose estimation methods [4][5][6][7] have demonstrated good performance. However, these methods generally focus on objects with known shapes and texture information, and require the corresponding CAD models.…”