mlsanityData to 14 Oct 2026

Generative 6D Pose Estimation via Conditional Flow Matching

arXiv 2602.19719 ↗First posted 23 Feb 2026cs.CVcited by 1

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

Existing methods for instance-level 6D pose estimation typically rely on neural networks that either directly regress the pose in SE(3) or estimate it indirectly via local feature matching. The former struggle with object symmetries, while the latter fail in the absence of distinctive local features. To overcome these limitations, we propose a novel formulation of 6D pose estimation as a conditional flow matching problem in ℝ3. We introduce Flose, a generative method that infers object poses via a denoising process conditioned on local features. While prior approaches based on conditional flow matching perform denoising solely based on geometric guidance, Flose integrates appearance-based semantic features to mitigate ambiguities caused by object symmetries. We further incorporate RANSAC-based registration to handle outliers. We validate Flose on five datasets from the established BOP benchmark. Flose outperforms prior methods with an average improvement of +4.5 Average Recall. Project Website : https://tev-fbk.github.io/Flose/

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 28 of its 34 references that are in the map. First posted on arXiv 23 Feb 2026.

Builds on

28 of 34 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching1 Dec

Other references · 27

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2022Scalable Diffusion Models with Transformerscited by 6,054
  4. 2022Flow Matching for Generative Modelingcited by 5,702
  5. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 775
  6. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  7. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 495
  8. 2020CosyPose: Consistent multi-view multi-object 6D pose estimationcited by 287
  9. 2018BOP: Benchmark for 6D Object Pose Estimationcited by 254
  10. 2019Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimationcited by 218
  11. 2017T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objectscited by 210
  12. 2021GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimatorcited by 148
15 more
  1. 2022ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimationcited by 116
  2. 2021SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddingscited by 95
  3. 20226-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmarkcited by 86
  4. 2015Recovering 6D Object Pose and Predicting Next-Best-View in the Crowdcited by 79
  5. 2024BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objectscited by 60
  6. 2022Coupled Iterative Refinement for 6D Multi-Object Pose Estimationcited by 48
  7. 2022Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimationcited by 32
  8. 2024DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose Estimationcited by 25
  9. 2022Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimationcited by 22
  10. 2025Rectified Point Flow: Generic Point Cloud Pose Estimationcited by 14
  11. 2020A Hybrid Approach for 6DoF Pose Estimationcited by 13
  12. 2025GARF: Learning Generalizable 3D Reassembly for Real-World Fracturescited by 13
  13. 2025Distilling 3D distinctive local descriptors for 6D pose estimationcited by 4
  14. 2025SE(3)-PoseFlow: Estimating 6D Pose Distributions for Uncertainty-Aware Robotic Manipulationcited by 4
  15. 2025HccePose(BF): Predicting Front & Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Foundational feature fusion for conditional flow matching in 6D pose estimationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Flow6D: Discrete-to-Continuous Flow Matching for Efficient and Accurate Category-Level 6D Pose Estimation
  2. 2026RFM-Pose:Reinforcement-Guided Flow Matching for Fast Category-Level 6D Pose Estimation
  3. 2023Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)
  4. 2024Category Level 6D Object Pose Estimation from a Single RGB Image using Diffusion
  5. 2024Particle-based 6D Object Pose Estimation from Point Clouds using Diffusion Models
  6. 2025AxisPose: Model-Free Matching-Free Single-Shot 6D Object Pose Estimation via Axis Generation
  7. 2026FMPose3D: monocular 3D pose estimation via flow matching
  8. 2025Joint Learning of Pose Regression and Denoising Diffusion with Score Scaling Sampling for Category-level 6D Pose Estimation