Strength
What it is useful for
- Convenient for reporting one overall score on historical benchmarks.
- Avoids applying inappropriate ADD errors to clearly symmetric objects.
6DoF / reporting protocol · protocol
ADD(S) / ADD(-S)
Symmetry-dependent ADD Reporting Protocol
Selects ADD or ADD-S according to object symmetry, then aggregates the results into one score.
A dataset-level reporting rule, not a new distance formula. Papers use ADD(S), ADD(-S), and ADD(S)-0.1d in different ways; consult the benchmark definition.
Principle
What error does this metric penalize?
The formulas define the computation. The diagrams show whether the error concerns a 3D model, visible surfaces, the image plane, or aggregation across thresholds.
Fix the rule for each object: ADD for asymmetric objects and ADD-S for symmetric objects.
Then average over objects or instances, so the overall score combines two error functions.
How to compute
Computation steps
- 01
Read object symmetries from the benchmark definition.
- 02
Compute ADD for asymmetric objects and ADD-S for symmetric objects.
- 03
Convert the selected errors to success rates or curves, then aggregate according to the protocol.
Caveat
Cautions when reading results
- Symmetry may be defined geometrically or modified by texture and annotation conventions; papers must explain their definition.
- Object sets and thresholds differ across benchmarks, so overall ADD(S) scores generally cannot be compared directly across datasets.
Paper atlas
Which papers report this metric?
Links are derived by normalizing the metric names used in each paper. Directly reported metrics are distinguished from metrics associated through a broader protocol.
35Related papers
35Directly reported
0Linked through a protocol
2026 Directly reported
ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors
CVPR · Liming Kuang, Yordanka Velikova, Mahdi Saleh et al.
Original terminology: ADD(-S) recall
2026 Directly reported
CoordAR: One-Reference 6D Pose Estimation of Novel Objects via Autoregressive Coordinate Map Generation
AAAI · Dexin Zuo, Ang Li, Wei Wang et al.
Original terminology: ADD(-S) Recall
2026 Directly reported
Cov2Pose: Leveraging Spatial Covariance for Direct Manifold-aware 6-DoF Object Pose Estimation
CVPR · Nassim Ali Ousalah, Peyman Rostami, Vincent Gaudillière et al.
Original terminology: ADD(-S) / AUC ADD(-S)
2026 Directly reported
EgoXtreme: A Dataset for Robust Object Pose Estimation in Egocentric Views under Extreme Conditions
CVPR · Taegyoon Yoon, Yegyu Han, Seojin Ji et al.
Original terminology: ADD(-S)
2026 Directly reported
Event6D: Event-based Novel Object 6D Pose Tracking
CVPR · Jae-Young Kang, Hoonhee Cho, Taeyeop Lee et al.
Original terminology: ADD(-S)
2026 Directly reported
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 et al.
Original terminology: ADD(-S) and ADD-S AUC
2026 Directly reported
Deep Learning-Based Object Pose Estimation: A Comprehensive Survey
IJCV · Jian Liu, Wei Sun, Hui Yang et al.
Original terminology: ADD(S) / ADD(-S) / ADD(S)
2026 Directly reported
OrienPose: Orientation-Guided Novel View Synthesis for Single-Image Unseen Object Pose Estimation
CVPR · Yating Liu, Zhaoshuai Qi, Yang Zou et al.
Original terminology: ADD(-S)
2026 Directly reported
PoseGaussian: 6D Pose Estimation for Unseen Objects via Sparse-View Object-Level 3D Gaussian Splatting
CVPR · Wubin Shi, Shaoyan Gai, Feipeng Da
Original terminology: ADD(S)-0.1d
2025 Directly reported
BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation
ICCV · Yuanhong Yu, Xingyi He, Chen Zhao et al.
Original terminology: ADD(s)-0.1d
2025 Directly reported
Deterministic Object Pose Confidence Region Estimation
ICCV · Jinghao Wang, Zhang Li, Zi Wang et al.
Original terminology: ADD(-S)
2025 Directly reported
Environment-Agnostic Pose: Generating Environment-independent Object Representations for 6D Pose Estimation
ICCV · Shaobo Zhang, Yuhang Huang, Wanqing Zhao et al.
Original terminology: ADD(-S)
2025 Directly reported
GS-Pose: Generalizable Segmentation-Based 6D Object Pose Estimation With 3D Gaussian Splatting
3DV · Dingding Cai, Janne Heikkilä, Esa Rahtu
Original terminology: ADD(S)@0.1d
2025 Directly reported
iG-6DoF: Model-free 6DoF Pose Estimation for Unseen Object via Iterative 3D Gaussian Splatting
CVPR · Tuo Cao, Fei Luo, Jiongming Qin et al.
Original terminology: ADD(S)-0.1d
2025 Directly reported
Occlusion-Aware 6D Pose Estimation with Depth-Guided Graph Encoding and Cross-Semantic Fusion for Robotic Grasping
ICRA · Jingyang Liu, Zhenyu Lu, Lu Chen et al.
Original terminology: ADD(-S) / ADD(-S) AUC
2025 Directly reported
ONDA-Pose: Occlusion-Aware Neural Domain Adaptation for Self-Supervised 6D Object Pose Estimation
CVPR · Tao Tan, Qiulei Dong
Original terminology: ADD(-S)
2025 Directly reported
RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation
ICCV · Junwen Huang, Shishir Reddy Vutukur, Peter KT Yu et al.
Original terminology: ADD(-S) Recall
2025 Directly reported
SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow
CVPR · Qingyuan Wang, Rui Song, Jiaojiao Li et al.
Original terminology: ADD(-S)
2025 Directly reported
Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation
IROS · Nassim Ali Ousalah, Anis Kacem, Enjie Ghorbel et al.
Original terminology: ADD(-S)-0.1d
2025 Directly reported
VAPO: Visibility-Aware Keypoint Localization for Efficient 6DoF Object Pose Estimation
IROS · Ruyi Lian, Yuewei Lin, Longin Jan Latecki et al.
Original terminology: ADD(-S) recall at 0.02d / 0.05d / 0.1d / AUC of ADD(-S)
2025 Directly reported
ZeroBP: Learning Position-Aware Correspondence for Zero-Shot 6D Pose Estimation in Bin-Picking
ICRA · Jianqiu Chen, Zikun Zhou, Xin Li et al.
Original terminology: AR of ADD(-S) / ADD(-S)-0.1d
2024 Directly reported
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation
CVPR · Li Xu, Haoxuan Qu, Yujun Cai et al.
Original terminology: ADD(-S) / AUC of ADD(-S)
2024 Directly reported
Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Images
3DV · Panwang Pan, Zhiwen Fan, Brandon Yushan Feng et al.
Original terminology: ADD(S)-0.1d
2024 Directly reported
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
CVPR · Yongliang Lin, Yongzhi Su, Praveen Nathan et al.
Original terminology: ADD(-S) recall / AUC of ADD(-S)
2024 Directly reported
Learning Better Keypoints for Multi-Object 6DoF Pose Estimation
WACV · Yangzheng Wu, Michael Greenspan
Original terminology: ADD(S) / ADD(S) AUC
2024 Directly reported
Effects of Markers in Training Datasets on the Accuracy of 6D Pose Estimation
WACV · Janis Rosskamp, Rene Weller, Gabriel Zachmann
Original terminology: ADD(S) / ADD(-S)
2024 Directly reported
MFOS: Model-Free & One-Shot Object Pose Estimation
AAAI · JongMin Lee, Yohann Cabon, Romain Brégier et al.
Original terminology: ADD(-S)-0.1d
2024 Directly reported
MOTPose: Multi-Object 6D Pose Estimation for Dynamic Video Sequences Using Attention-Based Temporal Fusion
ICRA · Arul Selvam Periyasamy, Sven Behnke
Original terminology: ADD(-S) AUC
2024 Directly reported
MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation
CVPR · Yuelong Li, Yafei Mao, Raja Bala et al.
Original terminology: ADD(-S) / AUC of ADD(-S)
2024 Directly reported
NeuSurfEmb: A Complete Pipeline for Dense Correspondence-Based 6D Object Pose Estimation without CAD Models
IROS · Francesco Milano, Jen Jen Chung, Hermann Blum et al.
Original terminology: ADD(-S) recall
2024 Directly reported
Open-Vocabulary Object 6D Pose Estimation
CVPR · Jaime Corsetti, Davide Boscaini, Changjae Oh et al.
Original terminology: ADD(S)-0.1d
2024 Directly reported
Pseudo-keypoint RKHS Learning for Self-supervised 6DoF Pose Estimation
ECCV · Yangzheng Wu, Michael Greenspan
Original terminology: ADD(S) / ADD(S) AUC
2024 Directly reported
SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation
CVPR · Jiehong Lin, Lihua Liu, Dekun Lu et al.
Original terminology: ADD(-S)
2024 Directly reported
Scalable Network and Adaptive Refinement Module for 6D Pose Estimation of Diverse Industrial Components
IROS · Kun Qian, Mustafa Suphi Erden, Xianwen Kong
Original terminology: ADD(-S)
2024 Directly reported
YOLO-6D-Pose: Enhancing YOLO for Single-Stage Monocular Multi-Object 6D Pose Estimation
3DV · Debapriya Maji, Soyeb N. Nagori, Manu Mathew et al.
Original terminology: ADD(-S) / ADD(-S) AUC
Next
Compare related metrics
Compare metrics at the same level to understand why one score improves while another stays unchanged.
ADD
Average Distance of Model Points
error
Compares each CAD point under predicted and ground-truth poses, averaging the 3D distances.
ADD-S
Average Closest Point Distance
error
Relaxes point identity: each ground-truth point is matched to the nearest point in the predicted model.
ADD / ADD-S AUC
Area Under the ADD Recall Curve
aggregate
Averages recall over a range of error thresholds instead of relying on a single threshold.