# Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data

arXiv: https://arxiv.org/abs/1812.00287 · HTML: https://mlsanity.com/paper/1812.00287/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2018.

## Built on by (most cited first, 30 of 68)

- [GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimator](https://mlsanity.com/paper/2102.12145/) (2021)
- [ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation](https://mlsanity.com/paper/2203.09418/) (2022)
- [RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild](https://mlsanity.com/paper/2208.05963/) (2022)
- [GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting](https://mlsanity.com/paper/2203.07918/) (2022)
- [SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation](https://mlsanity.com/paper/2108.08367/) (2021)
- [RelPose++: Recovering 6D Poses from Sparse-view Observations](https://mlsanity.com/paper/2305.04926/) (2023)
- [EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation](https://mlsanity.com/paper/2303.12787/) (2023)
- [Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold](https://mlsanity.com/paper/2106.05965/) (2021)
- [NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation](https://mlsanity.com/paper/2203.04802/) (2022)
- [HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios](https://mlsanity.com/paper/2212.10428/) (2022)
- [Wide-Baseline Relative Camera Pose Estimation with Directional Learning](https://mlsanity.com/paper/2106.03336/) (2021)
- [Deformable 3D Gaussian Splatting for Animatable Human Avatars](https://mlsanity.com/paper/2312.15059/) (2023)
- [RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation](https://mlsanity.com/paper/2208.00237/) (2022)
- [SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation](https://mlsanity.com/paper/2208.06661/) (2022)
- [Deep Learning-Based Object Pose Estimation: A Comprehensive Survey](https://mlsanity.com/paper/2405.07801/) (2024)
- [NOPE: Novel Object Pose Estimation from a Single Image](https://mlsanity.com/paper/2303.13612/) (2023)
- [Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation](https://mlsanity.com/paper/2203.10339/) (2022)
- [DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation](https://mlsanity.com/paper/2207.02805/) (2022)
- [MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images](https://mlsanity.com/paper/2403.01517/) (2024)
- [Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting](https://mlsanity.com/paper/2104.02527/) (2021)
- [SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs](https://mlsanity.com/paper/2309.12188/) (2023)
- [TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation](https://mlsanity.com/paper/2212.12902/) (2022)
- [Neural Correspondence Field for Object Pose Estimation](https://mlsanity.com/paper/2208.00113/) (2022)
- [Challenges for Monocular 6D Object Pose Estimation in Robotics](https://mlsanity.com/paper/2307.12172/) (2023)
- [GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation](https://mlsanity.com/paper/2502.04293/) (2025)
- [Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)](https://mlsanity.com/paper/2305.15873/) (2023)
- [Polarimetric Pose Prediction](https://mlsanity.com/paper/2112.03810/) (2021)
- [PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation](https://mlsanity.com/paper/2102.12096/) (2021)
- [DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency](https://mlsanity.com/paper/2104.03658/) (2021)
- [SpyroPose: SE(3) Pyramids for Object Pose Distribution Estimation](https://mlsanity.com/paper/2303.05308/) (2023)

## Abstract (arXiv)

3D object detection and pose estimation from a single image are two inherently ambiguous problems. Oftentimes, objects appear similar from different viewpoints due to shape symmetries, occlusion and repetitive textures. This ambiguity in both detection and pose estimation means that an object instance can be perfectly described by several different poses and even classes. In this work we propose to explicitly deal with this uncertainty. For each object instance we predict multiple pose and class outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures. The distribution collapses to a single outcome when the visual appearance uniquely identifies just one valid pose. We show the benefits of our approach which provides not only a better explanation for pose ambiguity, but also a higher accuracy in terms of pose estimation.