Papers › DPOD: 6D Pose Object Detector and Refiner

DPOD: 6D Pose Object Detector and Refiner

28 Feb 2019ICCV 2019 10arXiv:1902.11020archive 2025-07-28

Sergey Zakharov, Ivan Shugurov, Slobodan Ilic

In this paper we present a novel deep learning method for 3D object detection and 6D pose estimation from RGB images. Our method, named DPOD (Dense Pose Object Detector), estimates dense multi-class 2D-3D correspondence maps between an input image and available 3D models. Given the correspondences, a 6DoF pose is computed via PnP and RANSAC. An additional RGB pose refinement of the initial pose estimates is performed using a custom deep learning-based refinement scheme. Our results and comparison to a vast number of related works demonstrate that a large number of correspondences is beneficial for obtaining high-quality 6D poses both before and after refinement. Unlike other methods that mainly use real data for training and do not train on synthetic renderings, we perform evaluation on both synthetic and real training data demonstrating superior results before and after refinement when compared to all recent detectors. While being precise, the presented approach is still real-time capable.

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zakharos/DPOD officialmentioned on GitHubpytorchNOASSERTION report
yshah43/DPOD mentioned on GitHubpytorchMIT report

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ADD_score yshah43/DPOD/helper.py community (archive-listed) ran MIT (permissive) · 06014bd3ae5a2aee · report
Matching_loss yshah43/DPOD/pose_refinement.py community (archive-listed) ran MIT (permissive) · 435b1f8f8f5b7515 · report
fetch_ptcld_data yshah43/DPOD/pose_refinement.py community (archive-listed) ran MIT (permissive) · 578ba089e447bb21 · report
fill_holes yshah43/DPOD/create_ground_truth.py community (archive-listed) ran fingerprinted MIT (permissive) · b6a6fb23a76018c7 · report
get_rot_tra yshah43/DPOD/create_ground_truth.py community (archive-listed) ran MIT (permissive) · e9a8aafe5c3ef9dc · report
load_obj yshah43/DPOD/helper.py community (archive-listed) ran · our draft was wrong MIT (permissive) · b91a5961fb87cb5e · report
create_bounding_box yshah43/DPOD/helper.py community (archive-listed) unverified MIT (permissive) · f55979ffb2cdf732 · report
create_rendering yshah43/DPOD/create_renderings.py community (archive-listed) unverified MIT (permissive) · 9c8fd9c497bd1443 · report

Tasks

3D Object Detection6D Pose Estimation6D Pose Estimation using RGBObjectObject DetectionPose Estimationobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
6D Pose Estimation using RGB LineMOD DPOD Accuracy (ADD) 95.15% #8 of 22 Archive leaderboard report
6D Pose Estimation using RGB LineMOD DPOD Mean ADD 95.2 #8 of 22 Archive leaderboard report
6D Pose Estimation using RGB Occlusion LineMOD DPOD Mean ADD 47.25 #9 of 13 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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