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BOP / 6D localization 聚合 · aggregate

BOP AR

BOP Average Recall

先在多组阈值下计算 VSD、MSSD、MSPD 的 recall,再把它们聚合为 BOP 的定位分数。

AR 不是单个距离。读 BOP 表格时要先确认 AR_VSD、AR_MSSD、AR_MSPD、AR_D 还是跨数据集 AR_C;它们处在不同聚合层级。

Principle

原理:它究竟在惩罚什么?

公式给出计算规则;示意图帮助区分误差发生在三维模型、可见表面、图像平面还是阈值聚合层。

对每个误差函数,先判断预测 pose 在某个阈值下是否正确,再统计所有标注实例的 recall。

BOP 不押注单一应用需求:用可见表面、三维最坏偏差和二维可感知偏差三种视角共同评分。

ARD=ARVSD+ARMSSD+ARMSPD3,ARC=avg⁡D∈coreARDAR_D=\frac{AR_{VSD}+AR_{MSSD}+AR_{MSPD}}{3},\qquad AR_C=\operatorname*{avg}_{D\in\mathrm{core}} AR_D
AR_e 是相应误差函数在多组正确性阈值(VSD 还包括 τ)上的 recall 平均。具体阈值以 BOP 当前任务配置为准。
recall / precision阈值 / 置信度跨多个设置聚合
本站示意:用于理解该指标关注的误差空间,并非真实数据或原论文插图。

How to compute

计算步骤

  1. 01

    对每个测试实例计算 VSD、MSSD、MSPD。

  2. 02

    在 BOP 定义的多组阈值下统计每种 error 的 recall,并求 AR_e。

  3. 03

    平均三个 AR_e 得到数据集 AR_D;需要时再平均核心数据集得到 AR_C。

Strength

适合回答

  • 同时覆盖遮挡、对称、三维精度和图像投影。
  • 多阈值聚合比单一 success threshold 更稳定。

Caveat

读表时要警惕

  • “AR”单独出现时必须确认它的 dataset 范围、任务和 BOP 年份。
  • AR 用于 object localization;检测任务使用的是 AP,不能混写。

Paper atlas

文献库中哪些论文报告了它?

由论文的原始指标写法自动归一化得到。直接报告与通过上层协议关联的论文会明确区分。

在文献库筛选
53关联论文
53直接报告
0通过协议关联
2026 直接报告

AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment

CVPR · Anna Šárová Mikeštíková, Médéric Fourmy, Martin Cifka 等

原始写法:BOP AR
2026 直接报告

COG: Confidence-aware Optimal Geometric Correspondence for Unsupervised Single-reference Novel Object Pose Estimation

CVPR · Yuchen Che, Jingtu Wu, Hao Zheng 等

原始写法:BOP AR
2026 直接报告

ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors

CVPR · Liming Kuang, Yordanka Velikova, Mahdi Saleh 等

原始写法:BOP AR
2026 直接报告

CoordAR: One-Reference 6D Pose Estimation of Novel Objects via Autoregressive Coordinate Map Generation

AAAI · Dexin Zuo, Ang Li, Wei Wang 等

原始写法:AR (MSSD/MSPD/VSD)
2026 直接报告

Finding NeMO: A Geometry-Aware Representation of Template Views for Few-Shot Perception

3DV · Sebastian Jung, Leonard Klüpfel, Rudolph Triebel 等

原始写法:AR
2026 直接报告

HRDexDB: A Paired Human-Robot Dataset for Cross-Embodiment Dexterous Grasping

arXiv · Jongbin Lim, Taeyun Ha, Mingi Choi 等

原始写法:AR_MSSD
2026 直接报告

IndustryShapes: An RGB-D Benchmark Dataset for 6D Object Pose Estimation of Industrial Assembly Components and Tools

ICRA · Panagiotis Sapoutzoglou, Orestis Vaggelis, Athina Zacharia 等

原始写法:BOP AR
2026 直接报告

InstantPose: Zero-Shot Instance-Level 6D Pose Estimation From a Single View

ICRA / IEEE RA-L · Francesco Di Felice, Alberto Remus, Stefano Gasperini 等

原始写法:BOP AR
2026 直接报告

Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

IJCV · Jian Liu, Wei Sun, Hui Yang 等

原始写法:BOP AR
2026 直接报告

Pose Anything Anywhere: Model-free Object Poses from Arbitrary References

ECCV · Hongli Xu, Jiaqi Hu, Junwen Huang 等

原始写法:BOP AR
2026 直接报告

PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoning

CVPR · Jianqi Chen, Biao Zhang, Xiangjun Tang 等

原始写法:BOP AR
2026 直接报告

PoseGaussian: 6D Pose Estimation for Unseen Objects via Sparse-View Object-Level 3D Gaussian Splatting

CVPR · Wubin Shi, Shaoyan Gai, Feipeng Da

原始写法:AR
2026 直接报告

WALDO: Where Unseen Model-based 6D Pose Estimation Meets Occlusion

WACV · Sajjad Pakdamansavoji, Yintao Ma, Amir Rasouli 等

原始写法:BOP AR
2025 直接报告

Any6D: Model-free 6D Pose Estimation of Novel Objects

CVPR · Taeyeop Lee, Bowen Wen, Minjun Kang 等

原始写法:BOP AR / AR_VSD / MSSD / MSPD
2025 直接报告

Co-op: Correspondence-based Novel Object Pose Estimation

CVPR · Sungphill Moon, Hyeontae Son, Dongcheol Hur 等

原始写法:BOP AR / AR_VSD / AR_MSSD / AR_MSPD
2025 直接报告

Distilling 3D Distinctive Local Descriptors for 6D Pose Estimation

IROS · Amir Hamza, Andrea Caraffa, Davide Boscaini 等

原始写法:BOP AR
2025 直接报告

Multi-Layer Feature Exchange Transformer for Multi-View 6D Object Pose Estimation in Robot Bin Picking

ICRA · Momen Khalil, Vincent Dietrich, Slobodan Ilic

原始写法:BOP AR
2025 直接报告

Generalizable Zero-Shot Object Pose Estimation for Bin-Picking

ICRA · Zijiang Zhang, Huimin Lu, Jintong Cai 等

原始写法:BOP AR
2025 直接报告

iG-6DoF: Model-free 6DoF Pose Estimation for Unseen Object via Iterative 3D Gaussian Splatting

CVPR · Tuo Cao, Fei Luo, Jiongming Qin 等

原始写法:BOP AR_VSD / MSSD / MSPD
2025 直接报告

Improving 6D Object Pose Estimation of Metallic Household and Industry Objects

IROS · Thomas Pöllabauer, Michael Gasser, Tristan Wirth 等

原始写法:BOP AR
2025 直接报告

MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation

ICCV · Xinhang Liu, Jiawei Shi, Zheng Dang 等

原始写法:BOP AR
2025 直接报告

One2Any: One-Reference 6D Pose Estimation for Any Object

CVPR · Mengya Liu, Siyuan Li, Ajad Chhatkuli 等

原始写法:BOP AR
2025 直接报告

Pos3R: 6D Pose Estimation for Unseen Objects Made Easy

CVPR · Weijian Deng, Dylan Campbell, Chunyi Sun 等

原始写法:BOP AR / AR_VSD / AR_MSSD / AR_MSPD
2025 直接报告

RAG-6DPose: Retrieval-Augmented 6D Pose Estimation via Leveraging CAD as Knowledge Base

IROS · Kuanning Wang, Yuqian Fu, Tianyu Wang 等

原始写法:BOP AR
2025 直接报告

Shape-Biased Texture Agnostic Representations for Improved Textureless and Metallic Object Detection and 6D Pose Estimation

WACV · Peter Hönig, Stefan Thalhammer, Jean-Baptiste Weibel 等

原始写法:BOP AR
2025 直接报告

RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation

ICCV · Junwen Huang, Shishir Reddy Vutukur, Peter KT Yu 等

原始写法:BOP AR
2025 直接报告

Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation

WACV · Varun Burde, Assia Benbihi, Pavel Burget 等

原始写法:BOP AR
2025 直接报告

RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objects

CVPR · Jaeguk Kim, Jaewoo Park, Keuntek Lee 等

原始写法:BOP AR / AR_VSD / AR_MSSD / AR_MSPD
2025 直接报告

RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking

IROS · Teng Guo, Jingjin Yu

原始写法:BOP AR
2025 直接报告

SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow

CVPR · Qingyuan Wang, Rui Song, Jiaojiao Li 等

原始写法:BOP AR
2025 直接报告

UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References

CVPR · Ming-Feng Li, Xin Yang, Fu-En Wang 等

原始写法:AR
2025 直接报告

UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image

CVPR · Xingyu Liu, Gu Wang, Ruida Zhang 等

原始写法:BOP AR
2025 直接报告

ZeroPose: CAD-Prompted Zero-Shot Object 6D Pose Estimation in Cluttered Scenes

IEEE TCSVT · Jianqiu Chen, Zikun Zhou, Mingshan Sun 等

原始写法:BOP AR / BOP AR (VSD/MSSD/MSPD)
2024 直接报告

FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

CVPR · Bowen Wen, Wei Yang, Jan Kautz 等

原始写法:BOP AR / BOP AR (VSD/MSSD/MSPD)
2024 直接报告

FoundPose: Unseen Object Pose Estimation with Foundation Features

ECCV · Evin Pınar Örnek, Yann Labbé, Bugra Tekin 等

原始写法:BOP AR
2024 直接报告

FreeZe: Training-free Zero-shot 6D Pose Estimation with Geometric and Vision Foundation Models

ECCV · Andrea Caraffa, Davide Boscaini, Amir Hamza 等

原始写法:BOP AR
2024 直接报告

GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects

CVPR · Sungphill Moon, Hyeontae Son, Dongcheol Hur 等

原始写法:BOP AR
2024 直接报告

GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence

CVPR · Van Nguyen Nguyen, Thibault Groueix, Mathieu Salzmann 等

原始写法:BOP AR
2024 直接报告

HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation

CVPR · Yongliang Lin, Yongzhi Su, Praveen Nathan 等

原始写法:BOP AR
2024 直接报告

Learning Better Keypoints for Multi-Object 6DoF Pose Estimation

WACV · Yangzheng Wu, Michael Greenspan

原始写法:BOP AR
2024 直接报告

MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images

CVPR · Junwen Huang, Hao Yu, Kuan-Ting Yu 等

原始写法:BOP AR
2024 直接报告

MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation

CVPR · Yuelong Li, Yafei Mao, Raja Bala 等

原始写法:BOP AR
2024 直接报告

NeRF-Feat: 6D Object Pose Estimation using Feature Rendering

3DV · Shishir Reddy Vutukur, Heike Brock, Benjamin Busam 等

原始写法:BOP AR
2024 直接报告

NeuSurfEmb: A Complete Pipeline for Dense Correspondence-Based 6D Object Pose Estimation without CAD Models

IROS · Francesco Milano, Jen Jen Chung, Hermann Blum 等

原始写法:BOP AR
2024 直接报告

Open-Vocabulary Object 6D Pose Estimation

CVPR · Jaime Corsetti, Davide Boscaini, Changjae Oh 等

原始写法:BOP AR
2024 直接报告

PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments

ECCV · Yang You, Kai Xiong, Zhening Yang 等

原始写法:BOP AR
2024 直接报告

Pseudo-keypoint RKHS Learning for Self-supervised 6DoF Pose Estimation

ECCV · Yangzheng Wu, Michael Greenspan

原始写法:BOP AR
2024 直接报告

SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation

CVPR · Jiehong Lin, Lihua Liu, Dekun Lu 等

原始写法:BOP AR / BOP AR (VSD/MSSD/MSPD)
2024 直接报告

ZS6D: Zero-Shot 6D Object Pose Estimation Using Vision Transformers

ICRA · Philipp Ausserlechner, David Haberger, Stefan Thalhammer 等

原始写法:BOP AR
2022 直接报告

MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare

CoRL · Yann Labbé, Lucas Manuelli, Arsalan Mousavian 等

原始写法:BOP AR / BOP AR (VSD/MSSD/MSPD)
2021 直接报告

GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

CVPR · Gu Wang, Fabian Manhardt, Federico Tombari 等

原始写法:AR
2020 直接报告

CosyPose: Consistent Multi-view Multi-object 6D Pose Estimation

ECCV · Yann Labbé, Justin Carpentier, Mathieu Aubry 等

原始写法:AR
2020 直接报告

EPOS: Estimating 6D Pose of Objects with Symmetries

CVPR · Tomas Hodan, Daniel Barath, Jiri Matas

原始写法:AR
定义与协议来源展开查看原始论文、综述和官方评测页面。