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ADD-S

Average Closest Point Distance

不强制点对点身份;对每个真实点寻找预测模型中的最近点。

ADD-S 用最近点匹配缓解旋转对称或重复结构的等价姿态问题,是 YCB-Video 等数据集的常见指标。

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。

轴对称杯子旋转 180° 后,语义上可能仍是同一个姿态;ADD-S 不再要求同一个 CAD 点必须对齐同一个点。

它也会让部分并非严格对称、但局部形状重复的错误显得较小。

ADD ⁣- ⁣S⁡=1∣M∣∑x1∈Mmin⁡x2∈M∥(Rgtx1+tgt)−(R^x2+t^)∥2\operatorname{ADD\!\text{-}\!S}=\frac{1}{|\mathcal M|}\sum_{x_1\in\mathcal M}\min_{x_2\in\mathcal M}\left\lVert (R_{gt}x_1+t_{gt})-(\hat R x_2+\hat t)\right\rVert_2
最近点搜索通常用 KD-tree 或 GPU 邻近查询加速。它的“宽容”来自允许 x_1 与 x_2 不同。
真实 pose预测 pose 对应点距离 / 姿态分量误差
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

    分别变换真实与预测模型点集。

  2. 02

    对每个真实点查询预测点集中的最近邻。

  3. 03

    对最近邻距离求平均,并依协议得到 error、recall 或 AUC。

Strength

适合回答

  • 对常见对称物体比 ADD 更合理。
  • 仍保持三维模型空间中的直观距离解释。

Caveat

读表时要警惕

  • 最近点可能掩盖纹理定义的朝向差异,例如带标签的圆柱。
  • 它不是 BOP 那种显式全局对称集合;对复杂离散对称仍要看协议。

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
75关联论文
59直接报告
16通过协议关联
2026 直接报告

Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation

ICRA / IEEE RA-L · Thomas Jantos, Stephan Weiss, Jan Steinbrener

原始写法:ADD-S
2026 直接报告

Learning 6D Object Pose Estimation With Event Cameras Using Synthetic Data and Domain Randomization

ICRA / IEEE RA-L · Oussama Abdul Hay, Xiaoqian Huang, Muhammad Ahmed Humais 等

原始写法:ADD-S
2026 直接报告

Color-Pair Guided Robust Zero-Shot 6D Pose Estimation and Tracking of Cluttered Objects on Edge Devices

ICRA · Xingjian Yang, Ashis G. Banerjee

原始写法:ADD-S
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:ADD(-S) recall / ADD-S-AUC
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:ADD(-S) Recall / AUC ADD-S
2026 通过上层协议

Cov2Pose: Leveraging Spatial Covariance for Direct Manifold-aware 6-DoF Object Pose Estimation

CVPR · Nassim Ali Ousalah, Peyman Rostami, Vincent Gaudillière 等

原始写法:ADD(-S) / AUC ADD-S / AUC ADD(-S)
2026 直接报告

DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera Scenarios

ICRA / IEEE RA-L · Xiangting Meng, Jiaqi Yang, Mingshu Chen 等

原始写法:ADD-S
2026 通过上层协议

EgoXtreme: A Dataset for Robust Object Pose Estimation in Egocentric Views under Extreme Conditions

CVPR · Taegyoon Yoon, Yegyu Han, Seojin Ji 等

原始写法:ADD(-S) / ADD-S AUC
2026 通过上层协议

Event6D: Event-based Novel Object 6D Pose Tracking

CVPR · Jae-Young Kang, Hoonhee Cho, Taeyeop Lee 等

原始写法:ADD(-S)
2026 直接报告

GenHOI: Generalized Hand-Object Pose Estimation with Occlusion Awareness

ECCV · Hui Yang, Wei Sun, Jian Liu 等

原始写法:ADD-S
2026 直接报告

GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes

ICRA / IEEE RA-L · Seunghyeok Back, Joosoon Lee, Kangmin Kim 等

原始写法:ADD-S / ADD(-S) and ADD-S AUC
2026 直接报告

HIPPo: Harnessing Image-to-3D Priors for Model-Free Zero-Shot 6D Pose Estimation

ICRA / IEEE RA-L · Yibo Liu, Zhaodong Jiang, Binbin Xu 等

原始写法:ADD-S / ADD-S AUC
2026 直接报告

Towards in-the-wild Egocentric 3D Hand-Object Pose Estimation

ECCV · Siddhant Bansal, Zhifan Zhu, Shashank Tripathi 等

原始写法:ADD-S
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:ADD-S
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:ADD-S / ADD(S)
2026 通过上层协议

OrienPose: Orientation-Guided Novel View Synthesis for Single-Image Unseen Object Pose Estimation

CVPR · Yating Liu, Zhaoshuai Qi, Yang Zou 等

原始写法:ADD(-S)
2026 直接报告

Point2Pose: Occlusion-Recovering 6D Pose Tracking and 3D Reconstruction for Multiple Unknown Objects Via 2D Point Trackers

ECCV · Tzu-Yuan Lin, Ho Jae Lee, Kevin Doherty 等

原始写法:ADD-S / ADD-S AUC
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:ADD(S)-0.1d
2026 直接报告

TwinTrack: Bridging Vision and Contact Physics for Real-Time Tracking of Unknown Objects in Contact-Rich Scenes

ICRA · Wen Yang, Zhixian Xie, Yiting Wang 等

原始写法:ADD-S
2026 直接报告

VPHO: Joint Visual-Physical Cue Learning and Aggregation for Hand-Object Pose Estimation

AAAI · Jun Zhou, Chi Xu, Kaifeng Tang 等

原始写法:ADD-S
2025 直接报告

6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting

ICCV · Yufeng Jin, Vignesh Prasad, Snehal Jauhri 等

原始写法:ADD-S
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:AUC ADD-S
2025 直接报告

BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation

ICCV · Yuanhong Yu, Xingyi He, Chen Zhao 等

原始写法:ADD-S / ADD(s)-0.1d / ADD-S AUC
2025 直接报告

CRISP: Object Pose and Shape Estimation with Test-Time Adaptation

CVPR · Jingnan Shi, Rajat Talak, Harry Zhang 等

原始写法:ADD-S / ADD-S AUC
2025 通过上层协议

Deterministic Object Pose Confidence Region Estimation

ICCV · Jinghao Wang, Zhang Li, Zi Wang 等

原始写法:ADD(-S)
2025 直接报告

DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects

IROS · Tingbang Liang, Yixin Zeng, Jiatong Xie 等

原始写法:ADD-S / ADD-S AUC at 0.1 m
2025 通过上层协议

Environment-Agnostic Pose: Generating Environment-independent Object Representations for 6D Pose Estimation

ICCV · Shaobo Zhang, Yuhang Huang, Wanqing Zhao 等

原始写法:ADD(-S)
2025 直接报告

GS-Pose: Generalizable Segmentation-Based 6D Object Pose Estimation With 3D Gaussian Splatting

3DV · Dingding Cai, Janne Heikkilä, Esa Rahtu

原始写法:ADD-S / ADD(S)@0.1d
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:ADD(S)-0.1d
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:ADD-S
2025 直接报告

Visuo-Tactile Object Pose Estimation for a Multi-Finger Robot Hand with Low-Resolution In-Hand Tactile Sensing

ICRA · Lukas Mack, Felix Grüninger, Benjamin A. Richardson 等

原始写法:ADD-S
2025 直接报告

Occlusion-Aware 6D Pose Estimation with Depth-Guided Graph Encoding and Cross-Semantic Fusion for Robotic Grasping

ICRA · Jingyang Liu, Zhenyu Lu, Lu Chen 等

原始写法:ADD-S / ADD(-S) / ADD(-S) AUC
2025 通过上层协议

ONDA-Pose: Occlusion-Aware Neural Domain Adaptation for Self-Supervised 6D Object Pose Estimation

CVPR · Tao Tan, Qiulei Dong

原始写法:ADD(-S) / ADD-S AUC
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:ADD-S AUC
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:ADD(-S) Recall
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:ADD(-S) / ADD-S AUC
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:ADD-S AUC
2025 直接报告

Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation

IROS · Nassim Ali Ousalah, Anis Kacem, Enjie Ghorbel 等

原始写法:ADD-S / ADD(-S)-0.1d
2025 直接报告

VAPO: Visibility-Aware Keypoint Localization for Efficient 6DoF Object Pose Estimation

IROS · Ruyi Lian, Yuewei Lin, Longin Jan Latecki 等

原始写法:ADD-S / ADD(-S) recall at 0.02d / 0.05d / 0.1d / AUC of ADD(-S)
2025 直接报告

ViTa-Zero: Zero-Shot Visuotactile Object 6D Pose Estimation

ICRA · Hongyu Li, James Akl, Srinath Sridhar 等

原始写法:ADD-S / ADD-S AUC
2025 直接报告

ZeroBP: Learning Position-Aware Correspondence for Zero-Shot 6D Pose Estimation in Bin-Picking

ICRA · Jianqiu Chen, Zikun Zhou, Xin Li 等

原始写法:ADD-S / AR of ADD(-S) / ADD(-S)-0.1d
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:ADD-S
2024 直接报告

6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation

CVPR · Li Xu, Haoxuan Qu, Yujun Cai 等

原始写法:ADD-S / ADD(-S) / AUC of ADD(-S)
2024 直接报告

Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Images

3DV · Panwang Pan, Zhiwen Fan, Brandon Yushan Feng 等

原始写法:ADD-S / ADD(S)-0.1d
2024 直接报告

FAFA: Frequency-Aware Flow-Aided Self-Supervision for Underwater Object Pose Estimation

ECCV · Jingyi Tang, Gu Wang, Zeyu Chen 等

原始写法:ADD-S
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:ADD-S / ADD-S AUC
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:ADD-S / ADD(-S) recall / AUC of ADD(-S)
2024 直接报告

Learning Better Keypoints for Multi-Object 6DoF Pose Estimation

WACV · Yangzheng Wu, Michael Greenspan

原始写法:ADD-S / ADD(S) / ADD(S) AUC
2024 直接报告

L4D-Track: Language-to-4D Modeling Towards 6-DoF Tracking and Shape Reconstruction in 3D Point Cloud Stream

CVPR · Jingtao Sun, Yaonan Wang, Mingtao Feng 等

原始写法:ADD-S
2024 直接报告

Effects of Markers in Training Datasets on the Accuracy of 6D Pose Estimation

WACV · Janis Rosskamp, Rene Weller, Gabriel Zachmann

原始写法:ADD-S
2024 直接报告

MFOS: Model-Free & One-Shot Object Pose Estimation

AAAI · JongMin Lee, Yohann Cabon, Romain Brégier 等

原始写法:ADD-S / ADD(-S)-0.1d
2024 直接报告

MOTPose: Multi-Object 6D Pose Estimation for Dynamic Video Sequences Using Attention-Based Temporal Fusion

ICRA · Arul Selvam Periyasamy, Sven Behnke

原始写法:ADD-S / ADD-S AUC / ADD(-S) AUC
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:ADD-S / ADD(-S) / AUC of ADD(-S)
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:ADD-S
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:ADD-S / ADD(-S) recall
2024 直接报告

Open-Vocabulary Object 6D Pose Estimation

CVPR · Jaime Corsetti, Davide Boscaini, Changjae Oh 等

原始写法:ADD-S / ADD(S)-0.1d
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:ADD-S / ADD-S AUC
2024 直接报告

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

ECCV · Yangzheng Wu, Michael Greenspan

原始写法:ADD-S / ADD(S) / ADD(S) AUC / ADD-S AUC
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:ADD-S / ADD(-S)
2024 直接报告

Scalable Network and Adaptive Refinement Module for 6D Pose Estimation of Diverse Industrial Components

IROS · Kun Qian, Mustafa Suphi Erden, Xianwen Kong

原始写法:ADD-S / ADD(-S)
2024 直接报告

YOLO-6D-Pose: Enhancing YOLO for Single-Stage Monocular Multi-Object 6D Pose Estimation

3DV · Debapriya Maji, Soyeb N. Nagori, Manu Mathew 等

原始写法:ADD-S / ADD(-S) / ADD(-S) AUC
2022 直接报告

MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Network

arXiv · Fabian Duffhauss, Tobias Demmler, Gerhard Neumann

原始写法:ADD-S
2022 直接报告

PIZZA: A Powerful Image-only Zero-Shot Zero-CAD Approach to 6 DoF Tracking

3DV · Van Nguyen Nguyen, Yuming Du, Yang Xiao 等

原始写法:ADD-S / ADD-S AUC
2022 直接报告

YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression

arXiv · Arash Amini, Arul Selvam Periyasamy, Sven Behnke

原始写法:ADD-S
2021 直接报告

FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation

CVPR · Yisheng He, Haibin Huang, Haoqiang Fan 等

原始写法:ADD-S
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:ADD-S
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:ADD-S
2020 直接报告

PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation

CVPR · Yisheng He, Wei Sun, Haibin Huang 等

原始写法:ADD-S
2019 直接报告

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

CVPR · Chen Wang, Danfei Xu, Yuke Zhu 等

原始写法:ADD-S
2019 直接报告

DPOD: 6D Pose Object Detector and Refiner

ICCV · Sergey Zakharov, Ivan Shugurov, Slobodan Ilic

原始写法:ADD-S
2019 直接报告

PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation

CVPR · Sida Peng, Yuan Liu, Qixing Huang 等

原始写法:ADD-S
2018 直接报告

Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects

CoRL · Jonathan Tremblay, Thang To, Balakumar Sundaralingam 等

原始写法:ADD-S
2018 直接报告

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

RSS · Yu Xiang, Tanner Schmidt, Venkatraman Narayanan 等

原始写法:ADD-S
2018 直接报告

Real-Time Seamless Single Shot 6D Object Pose Prediction

CVPR · Bugra Tekin, Sudipta N. Sinha, Pascal Fua

原始写法:ADD-S
2017 直接报告

SSD-6D: Making RGB-based 3D Detection and 6D Pose Estimation Great Again

ICCV · Wadim Kehl, Fabian Manhardt, Federico Tombari 等

原始写法:ADD-S
定义与协议来源展开查看原始论文、综述和官方评测页面。