{"url":"/dataset/linemod-1","name":"LM","full_name":"LINEMOD","description_markdown":"The **LM (Linemod) dataset** is a valuable resource introduced by Stefan Hinterstoisser and colleagues in their research on **model-based training, detection, and pose estimation of texture-less 3D objects in heavily cluttered scenes**¹. Let's delve into the details:\r\n\r\n1. **Purpose and Context**:\r\n   - The primary goal of the LM dataset is to facilitate the development and evaluation of methods for detecting and estimating the **6 degrees-of-freedom pose** of texture-less 3D objects.\r\n   - It specifically targets scenarios where objects lack distinctive textures and are embedded in complex backgrounds with occlusions.\r\n\r\n2. **Methodology**:\r\n   - The dataset builds upon the **LINEMOD approach**, which combines depth and color information to create templates representing different views of an object.\r\n   - LINEMOD templates are learned from 3D models and serve as a basis for object detection.\r\n   - The initial LINEMOD method had limitations, including online template learning and approximate pose estimation.\r\n\r\n3. **Improvements and Contributions**:\r\n   - Hinterstoisser et al. enhance LINEMOD by incorporating accurate 3D models of objects.\r\n   - Their approach leverages the 3D model to address the shortcomings of the original LINEMOD.\r\n   - Notable improvements include better pose estimation and reduced false positives.\r\n   - The proposed framework is suitable for **robotics applications**, such as object manipulation.\r\n\r\n4. **Dataset Details**:\r\n   - The LM dataset consists of **15 registered video sequences**, each containing **over 1100 frames**.\r\n   - These sequences feature **15 different texture-less household objects**.\r\n   - Objects in the dataset exhibit discriminative color, shape, and size characteristics.\r\n   - Researchers can use this dataset to evaluate and compare their methods for object detection and pose estimation.\r\n\r\n![LM Dataset Example](https://i.imgur.com/EXAMPLE_IMAGE.png)\r\n\r\nIn summary, the LM dataset provides a valuable benchmark for advancing the field of 6D object pose estimation, especially in scenarios where texture information is limited¹². Researchers can access this dataset to test and refine their algorithms, ultimately contributing to advancements in robotics and machine vision.\r\n\r\n(1) Model Based Training, Detection and Pose ... - Stefan HINTERSTOISSER. http://stefan-hinterstoisser.com/papers/hinterstoisser2012accv.pdf.\r\n(2) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.\r\n(3) paroj/linemod_dataset: Hinterstoisser et al. ACCV12 dataset - GitHub. https://github.com/paroj/linemod_dataset.","description_withheld":null,"homepage":"https://bop.felk.cvut.cz/datasets","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"6D Pose Estimation","url":"/task/6d-pose-estimation-1","datasets_with_task":"/datasets/task/6d-pose-estimation-1"},{"name":"6D Pose Estimation using RGBD","url":"/task/6d-pose-estimation-using-rgbd","datasets_with_task":"/datasets/task/6d-pose-estimation-using-rgbd"},{"name":"6D Pose Estimation using RGB","url":"/task/6d-pose-estimation","datasets_with_task":"/datasets/task/6d-pose-estimation"}],"languages":[],"variants":["LineMOD","Occlusion LineMOD","Synth Objects-to-LINEMOD","LM"],"data_loaders":[],"num_papers_in_archive":34,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/6d-pose-estimation-on-linemod","task":"6D Pose Estimation using RGB","dataset_variant":"LineMOD","rows":22,"metrics":["Mean ADD","Accuracy (ADD)","Accuracy","Mean IoU"],"first_row_in_archive_order":{"model":"RNNPose","paper":"/paper/rnnpose-recurrent-6-dof-object-pose","metrics":{"Accuracy (ADD)":"97.37%","Mean ADD":"97.37"},"code_links":[{"title":"decayale/rnnpose","url":"https://github.com/decayale/rnnpose"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/6d-pose-estimation-using-rgb-on-occlusion","task":"6D Pose Estimation using RGB","dataset_variant":"Occlusion LineMOD","rows":13,"metrics":["Mean ADD"],"first_row_in_archive_order":{"model":"SO-Pose","paper":"/paper/so-pose-exploiting-self-occlusion-for-direct","metrics":{"Mean ADD":"62.3"},"code_links":[{"title":"THU-DA-6D-Pose-Group/GDR-Net","url":"https://github.com/THU-DA-6D-Pose-Group/GDR-Net"},{"title":"shangbuhuan13/so-pose","url":"https://github.com/shangbuhuan13/so-pose"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/6d-pose-estimation-using-rgbd-on-linemod","task":"6D Pose Estimation using RGBD","dataset_variant":"LineMOD","rows":8,"metrics":["Mean ADD","Mean ADD-S","Mean IoU"],"first_row_in_archive_order":{"model":"RCVPose+ICP","paper":"/paper/vote-from-the-center-6-dof-pose-estimation-in","metrics":{"Mean ADD":"99.7"},"code_links":[{"title":"aaronwool/rcvpose","url":"https://github.com/aaronwool/rcvpose"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/6d-pose-estimation-on-linemod-2","task":"6D Pose Estimation","dataset_variant":"LineMOD","rows":5,"metrics":["Accuracy (ADD)","Mean ADD-S"],"first_row_in_archive_order":{"model":"FFB6D","paper":"/paper/ffb6d-a-full-flow-bidirectional-fusion","metrics":{"Accuracy (ADD)":"99.7"},"code_links":[{"title":"ethnhe/PVN3D","url":"https://github.com/ethnhe/PVN3D"},{"title":"ethnhe/FFB6D","url":"https://github.com/ethnhe/FFB6D"},{"title":"hz-ants/FFB6D","url":"https://github.com/hz-ants/FFB6D"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/domain-adaptation-on-synth-objects-to-linemod","task":"Domain Adaptation","dataset_variant":"Synth Objects-to-LINEMOD","rows":1,"metrics":["Classification Accuracy","Mean Angle Error"],"first_row_in_archive_order":{"model":"DSN (DANN)","paper":"/paper/domain-separation-networks","metrics":{"Classification Accuracy":"100","Mean Angle Error":"53.27"},"code_links":[{"title":"tensorflow/models","url":"https://github.com/tensorflow/models"},{"title":"tensorflow/models","url":"https://github.com/tensorflow/models/tree/master/research/domain_adaptation"},{"title":"fungtion/DSN","url":"https://github.com/fungtion/DSN"},{"title":"AmirHussein96/Simplified-DSN","url":"https://github.com/AmirHussein96/Simplified-DSN"},{"title":"WinChua/CDRTR","url":"https://github.com/WinChua/CDRTR"},{"title":"better-chao/DSN","url":"https://github.com/better-chao/DSN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/yolov5-6d-advancing-6-dof-instrument-pose","title":"YOLOv5-6D: Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries","date":"2024-03-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/epro-pnp-generalized-end-to-end-probabilistic-1","title":"EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation","date":"2023-03-22","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":8,"samples_ran":3,"samples_unverified":5,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rnnpose-recurrent-6-dof-object-pose","title":"RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization","date":"2022-03-24","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/epro-pnp-generalized-end-to-end-probabilistic","title":"EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation","date":"2022-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/gpv-pose-category-level-object-pose","title":"GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting","date":"2022-03-15","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/occlusion-robust-object-pose-estimation-with","title":"Occlusion-Robust Object Pose Estimation with Holistic Representation","date":"2021-10-22","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/so-pose-exploiting-self-occlusion-for-direct","title":"SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation","date":"2021-08-18","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":16,"samples_ran":8,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/vote-from-the-center-6-dof-pose-estimation-in","title":"Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting","date":"2021-04-06","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/repose-real-time-iterative-rendering-and","title":"RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering","date":"2021-04-01","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":10,"samples_ran":8,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ffb6d-a-full-flow-bidirectional-fusion","title":"FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation","date":"2021-03-03","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":8,"samples_ran":1,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/gdr-net-geometry-guided-direct-regression","title":"GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimator","date":"2021-02-24","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/end-to-end-differentiable-6dof-object-pose","title":"End-to-End Differentiable 6DoF Object Pose Estimation with Local and Global Constraints","date":"2020-11-22","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/efficientpose-an-efficient-accurate-and","title":"EfficientPose: An efficient, accurate and scalable end-to-end 6D multi object pose estimation approach","date":"2020-11-09","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/pose-proposal-critic-robust-pose-refinement","title":"Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors","date":"2020-05-13","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/hybridpose-6d-object-pose-estimation-under","title":"HybridPose: 6D Object Pose Estimation under Hybrid Representations","date":"2020-01-07","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":5,"samples_ran":5,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/w-posenet-dense-correspondence-regularized","title":"W-PoseNet: Dense Correspondence Regularized Pixel Pair Pose Regression","date":"2019-12-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/maskedfusion-mask-based-6d-object-pose","title":"MaskedFusion: Mask-based 6D Object Pose Estimation","date":"2019-11-18","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/pvn3d-a-deep-point-wise-3d-keypoints-voting","title":"PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation","date":"2019-11-11","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cdpn-coordinates-based-disentangled-pose","title":"CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation","date":"2019-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cullnet-calibrated-and-pose-aware-confidence","title":"CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation","date":"2019-09-30","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/bpnp-further-empowering-end-to-end-learning","title":"End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization","date":"2019-09-13","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pix2pose-pixel-wise-coordinate-regression-of","title":"Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation","date":"2019-08-20","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":8,"samples_ran":3,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dpod-dense-6d-pose-object-detector-in-rgb","title":"DPOD: 6D Pose Object Detector and Refiner","date":"2019-02-28","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":8,"samples_ran":6,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/implicit-3d-orientation-learning-for-6d","title":"Implicit 3D Orientation Learning for 6D Object Detection from RGB Images","date":"2019-02-04","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; 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not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":18,"samples_harvested":148,"samples_ran":65,"samples_unverified":83,"pointer_only_for_licence":15,"papers_with_no_sample_that_ran":5,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}