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BOP / 三维表面误差 · error

MSSD

Maximum Symmetry-Aware Surface Distance

在所有允许的全局对称变换中,取最小的“最大三维表面偏差”。

MSSD 对最坏点很敏感,并显式使用模型提供的对称集合;它强调三维几何是否足够精确,常用于机器人操作相关的严格评测。

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。

先对所有允许的对称变换 S 尝试对齐,避免把等价 pose 当成错误。

再看最坏一个模型顶点偏了多远,因此局部大错不会被平均值稀释。

eMSSD=min⁡S∈SM max⁡x∈VM∥P^x−PˉSx∥2e_{MSSD}=\min_{S\in\mathcal S_M}\ \max_{x\in\mathcal V_M}\left\lVert \hat P x-\bar P Sx\right\rVert_2
S 是模型的全局对称变换,V_M 是模型顶点集;max 使它成为“最坏情况”指标。
在允许对称变换中,选误差最小者
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

    读取 BOP models_info.json 中的离散/连续对称定义。

  2. 02

    对每个对称变换计算所有模型顶点的最大距离。

  3. 03

    取所有对称候选中的最小值,并按 BOP 阈值计算 recall。

Strength

适合回答

  • 显式、可复现地处理模型对称性。
  • 对安全距离、插入和抓取等不容许局部大错的任务有解释力。

Caveat

读表时要警惕

  • 对采样与极端顶点敏感,不能简单和 ADD 的均值数值对照。
  • 完整模型度量不表示被遮挡区域在图像中可辨。

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
45关联论文
30直接报告
15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 / MSSD
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 / MSSD
2026 通过上层协议

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 通过上层协议

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 / MSSD
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:MSSD / 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:MSSD
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:MSSD
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:MSSD / BOP AR
2026 通过上层协议

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 通过上层协议

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:MSSD
2026 直接报告

XYZ-IBD: Benchmarking Robust 6D Object Pose Estimation under Real-World Industrial Complexity

ECCV · Junwen Huang, Jiaqi Hu, Peter KT Yu 等

原始写法:MSSD
2025 通过上层协议

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 通过上层协议

Co-op: Correspondence-based Novel Object Pose Estimation

CVPR · Sungphill Moon, Hyeontae Son, Dongcheol Hur 等

原始写法:BOP AR / AR_MSSD
2025 通过上层协议

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

JENGA: Object Selection and Pose Estimation for Robotic Grasping from a Stack

IROS · Sai Srinivas Jeevanandam, Sandeep Inuganti, Shreedhar Govil 等

原始写法:MSSD
2025 通过上层协议

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 通过上层协议

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

CVPR · Mengya Liu, Siyuan Li, Ajad Chhatkuli 等

原始写法:BOP AR
2025 通过上层协议

Pos3R: 6D Pose Estimation for Unseen Objects Made Easy

CVPR · Weijian Deng, Dylan Campbell, Chunyi Sun 等

原始写法:BOP AR / AR_MSSD
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:MSSD
2025 通过上层协议

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:MSSD
2025 通过上层协议

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_MSSD
2025 通过上层协议

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 通过上层协议

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 通过上层协议

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:MSSD / 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:MSSD / 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:MSSD
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:MSSD
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:MSSD
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:MSSD
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:MSSD
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:MSSD
2024 直接报告

Open-Vocabulary Object 6D Pose Estimation

CVPR · Jaime Corsetti, Davide Boscaini, Changjae Oh 等

原始写法:MSSD
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:MSSD
2024 直接报告

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

ECCV · Yangzheng Wu, Michael Greenspan

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

Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)

CVPR · Tsu-Ching Hsiao, Hao-Wei Chen, Hsuan-Kung Yang 等

原始写法:MSSD
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:MSSD
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:MSSD / 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:MSSD / 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:MSSD / 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:MSSD / 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。