# DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose Estimation

arXiv: https://arxiv.org/abs/2402.12647 · HTML: https://mlsanity.com/paper/2402.12647/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 49 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [Geometric Loss Functions for Camera Pose Regression with Deep Learning](https://mlsanity.com/paper/1704.00390/) (2017)
- [Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation](https://mlsanity.com/paper/1908.07433/) (2019)
- [Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation](https://mlsanity.com/paper/2007.08454/) (2020)
- [EPOS: Estimating 6D Pose of Objects with Symmetries](https://mlsanity.com/paper/2004.00605/) (2020)
- [Image-based localization using LSTMs for structured feature correlation](https://mlsanity.com/paper/1611.07890/) (2016)
- [FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism](https://mlsanity.com/paper/2103.07054/) (2021)
- [ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation](https://mlsanity.com/paper/2203.09418/) (2022)
- [DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency](https://mlsanity.com/paper/2103.06526/) (2021)
- [Category Level Object Pose Estimation via Neural Analysis-by-Synthesis](https://mlsanity.com/paper/2008.08145/) (2020)
- [Fast Interactive Object Annotation with Curve-GCN](https://mlsanity.com/paper/1903.06874/) (2019)
- [GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting](https://mlsanity.com/paper/2203.07918/) (2022)
- [ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization](https://mlsanity.com/paper/2207.13691/) (2022)
- [CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation](https://mlsanity.com/paper/2203.01929/) (2022)
- [Learning Descriptors for Object Recognition and 3D Pose Estimation](https://mlsanity.com/paper/1502.05908/) (2015)
- [HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation](https://mlsanity.com/paper/2303.15743/) (2023)

## Built on by (most cited first, 25 of 25)

- [One2Any: One-Reference 6D Pose Estimation for Any Object](https://mlsanity.com/paper/2505.04109/) (2025)
- [Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation](https://mlsanity.com/paper/2502.02525/) (2025)
- [Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations](https://mlsanity.com/paper/2604.27106/) (2026)
- [GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation](https://mlsanity.com/paper/2503.15110/) (2025)
- [MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Model](https://mlsanity.com/paper/2504.10433/) (2025)
- [MK-Pose: Category-Level Object Pose Estimation via Multimodal-Based Keypoint Learning](https://mlsanity.com/paper/2507.06662/) (2025)
- [RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation](https://mlsanity.com/paper/2510.18521/) (2025)
- [SE(3)-PoseFlow: Estimating 6D Pose Distributions for Uncertainty-Aware Robotic Manipulation](https://mlsanity.com/paper/2511.01501/) (2025)
- [BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation](https://mlsanity.com/paper/2504.07955/) (2025)
- [fCOP: Focal Length Estimation from Category-level Object Priors](https://mlsanity.com/paper/2409.19641/) (2024)
- [Simultaneous Pick and Place Detection by Combining SE(3) Diffusion Models with Differential Kinematics](https://mlsanity.com/paper/2504.19502/) (2025)
- [OmniShape: Zero-Shot Multi-Hypothesis Shape and Pose Estimation in the Real World](https://mlsanity.com/paper/2508.03669/) (2025)
- [Joint Learning of Pose Regression and Denoising Diffusion with Score Scaling Sampling for Category-level 6D Pose Estimation](https://mlsanity.com/paper/2510.04125/) (2025)
- [Generative 6D Pose Estimation via Conditional Flow Matching](https://mlsanity.com/paper/2602.19719/) (2026)
- [SABER-6D: Shape Representation Based Implicit Object Pose Estimation](https://mlsanity.com/paper/2408.05867/) (2024)
- [DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera Scenarios](https://mlsanity.com/paper/2503.19625/) (2025)
- [Category-Level and Open-Set Object Pose Estimation for Robotics](https://mlsanity.com/paper/2504.19572/) (2025)
- [GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity](https://mlsanity.com/paper/2505.11905/) (2025)
- [Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation](https://mlsanity.com/paper/2508.14358/) (2025)
- [SCOPE: Semantic Conditioning for Sim2Real Category-Level Object Pose Estimation in Robotics](https://mlsanity.com/paper/2509.24572/) (2025)
- [Demo-Pose: Depth-Monocular Modality Fusion For Object Pose Estimation](https://mlsanity.com/paper/2603.27533/) (2026)
- [Unsupervised Domain Adaptation for Sim-to-Real Object Pose Estimation with Contrastive Alignment and Pseudo-Label Refinement](https://mlsanity.com/paper/2606.21287/) (2026)
- [LEGAU: Learning Semantic Gaussian Priors for Scalable Category-level Pose Estimation](https://mlsanity.com/paper/2609.35046/) (2026)
- [GenCOPE: Syn2Real Generalized Category-Level Object Pose Estimation for Robotic Picking](https://mlsanity.com/paper/2610.01758/) (2026)
- [SchemaPose: RGB-Based Category-Level Pose Estimation with Parametric Category Schema](https://mlsanity.com/paper/2610.18374/) (2026)

## Abstract (arXiv)

This paper addresses the challenging problem of category-level pose estimation. Current state-of-the-art methods for this task face challenges when dealing with symmetric objects and when attempting to generalize to new environments solely through synthetic data training. In this work, we address these challenges by proposing a probabilistic model that relies on diffusion to estimate dense canonical maps crucial for recovering partial object shapes as well as establishing correspondences essential for pose estimation. Furthermore, we introduce critical components to enhance performance by leveraging the strength of the diffusion models with multi-modal input representations. We demonstrate the effectiveness of our method by testing it on a range of real datasets. Despite being trained solely on our generated synthetic data, our approach achieves state-of-the-art performance and unprecedented generalization qualities, outperforming baselines, even those specifically trained on the target domain.