Papers › Estimating 6D Pose From Localizing Designated Surface Keypoints

Estimating 6D Pose From Localizing Designated Surface Keypoints

4 Dec 2018arXiv:1812.01387archive 2025-07-28

Zelin Zhao, Gao Peng, Haoyu Wang, Hao-Shu Fang, Chengkun Li, Cewu Lu

In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image. The core of our approach is that we first designate a set of surface points on target object model as keypoints and then train a keypoint detector (KPD) to localize them. Finally a PnP algorithm can recover the 6D pose according to the 2D-3D relationship of keypoints. Different from recent state-of-the-art CNN-based approaches that rely on a time-consuming post-processing procedure, our method can achieve competitive accuracy without any refinement after pose prediction. Meanwhile, we obtain a 30% relative improvement in terms of ADD accuracy among methods without using refinement. Moreover, we succeed in handling heavy occlusion by selecting the most confident keypoints to recover the 6D pose. For the sake of reproducibility, we will make our code and models publicly available soon.

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sjtuytc/betapose officialmentioned on GitHubpytorchMIT report
AP-EPFL/DA-segmentation-driven-pose mentioned on GitHubpytorchMIT report
hz-ants/betapose mentioned on GitHubpytorch report
hz-ants/segmentation-driven-pose-train- mentioned on GitHubpytorchMIT report
sjtuytc/segmentation-driven-pose mentioned on GitHubpytorchGPL-3.0 report
why2011btv/6d_pose_estimation mentioned on GitHubpytorchNOASSERTION report

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filter_result sjtuytc/betapose/3_6Dpose_estimator/pPose_nms.py official repository unverified MIT (permissive) · 8622d93ca4b78830 · report
getTime sjtuytc/betapose/3_6Dpose_estimator/fn.py official repository unverified MIT (permissive) · 2b0e5d20a6ff6bf0 · report
icp_refinement sjtuytc/betapose/2_keypoint_annotator/utils/detector.py official repository unverified MIT (permissive) · edbdf03d148b9c19 · report
p_merge sjtuytc/betapose/3_6Dpose_estimator/pPose_nms.py official repository unverified MIT (permissive) · 04c002fef331916f · report
parse_cfg sjtuytc/betapose/3_6Dpose_estimator/yolo/darknet.py official repository unverified MIT (permissive) · 4587b69d57d6511b · report
pose_nms sjtuytc/betapose/3_6Dpose_estimator/pPose_nms.py official repository unverified MIT (permissive) · 0b1a6e2926909c9f · report
load_yaml hz-ants/betapose/3_6Dpose_estimator/betapose_evaluate.py community (archive-listed) unverified MIT (permissive) · 42001485c89d0e7f · report

Tasks

6D Pose Estimation6D Pose Estimation using RGBPose EstimationPose Prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
6D Pose Estimation using RGB LineMOD Keypoint Detector Localization Accuracy 94.5% #17 of 22 Archive leaderboard report
6D Pose Estimation using RGB LineMOD Keypoint Detector Localization Mean ADD 72.6 #17 of 22 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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