# Robust 6D Object Pose Estimation by Learning RGB-D Features Source: [Robust 6D Object Pose Estimation by Learning RGB-D Features]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:09:20.078Z Authors: Meng Tian, Liang Pan, Marcelo H Ang Jr, Gim Hee Lee Published: 2020-02-29T06:24:55.000Z ## Abstract Accurate 6D object pose estimation is fundamental to robotic manipulation and grasping. Previous methods follow a local optimization approach which minimizes the distance between closest point pairs to handle the rotation ambiguity of symmetric objects. In this work, we propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Additionally, the object location is detected by aggregating point-wise vectors pointing to the 3D center. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches. Our code is available at https://github.com/mentian/object-posenet. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [6DoF Pose Estimation on Synthetic YCB Challenging Single Object](): 6DoF Pose Estimation; Synthetic YCB Challenging Single Object; Acc @ 0.5cm Thresh: 3. ## Original sources and implementation [Original paper]() [PDF]()