Generalizable 6D Pose Estimation of Textureless Objects with Planar-based Gaussian Splatting

2026-09-07

AI Summary

本文提出PG-Pose，通过结合基于平面的高斯点云重建和几何驱动的姿态优化方法，解决了无纹理物体6D姿态估计的问题。

Abstract

Estimating the 6D pose of textureless objects without prior CAD models remains a critical challenge due to the lack of appearance features. While recent generalizable approaches alleviate the dependence on object-specific models, their performance on low-texture objects is often limited by insufficient geometric constraints in the underlying representations. In this work, we propose PG-Pose, a geometry-aware framework combining Planar-based Gaussian Splatting (PGS) reconstruction and Geometry-driven pose optimization. In the offline representation extraction stage, three distinct representations of the object are extracted from multi-view reference RGB images with known poses. PG-Pose reconstructs a 3D Gaussian representation and renders high-fidelity depth maps to generate 3D point clouds through back projection. In the online pose inference stage, the initial pose of the input image is estimated by 2D-3D correspondence matching between the input image and the reconstructed 3D point clouds, followed by a PGS-Refiner for iterative pose optimization. Evaluations on the OnePose-LowTexture datasets, PG-Pose achieves an average accuracy of 94.2% ADD(S)@0.1d, with a 2.1% improvement average accuracy compared with the state-of-the-art (SOTA) GS-based approach. To further demonstrate the effectiveness of PG-Pose for industrial robots in grasping tasks, we deploy it on a dual-arm industrial robot and successfully realize the grasping task on an unseen object.

Problem

• 6D Pose Estimation
• Textureless Objects
• Geometric Constraints
• Generalizable Approaches

Innovation

• Planar-based Gaussian Splatting
• Geometry-driven pose optimization
• 2D-3D correspondence matching
• PGS-Refiner