# GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects

arXiv: https://arxiv.org/abs/2403.11510 · HTML: https://mlsanity.com/paper/2403.11510/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 68 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 30 of 47)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [CosyPose: Consistent multi-view multi-object 6D pose estimation](https://mlsanity.com/paper/2008.08465/) (2020)
- [DeepIM: Deep Iterative Matching for 6D Pose Estimation](https://mlsanity.com/paper/1804.00175/) (2018)
- [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)
- [T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects](https://mlsanity.com/paper/1701.05498/) (2017)
- [MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare](https://mlsanity.com/paper/2212.06870/) (2022)
- [BOP Challenge 2020 on 6D Object Localization](https://mlsanity.com/paper/2009.07378/) (2020)
- [DKM: Dense Kernelized Feature Matching for Geometry Estimation](https://mlsanity.com/paper/2202.00667/) (2022)
- [OnePose: One-Shot Object Pose Estimation without CAD Models](https://mlsanity.com/paper/2205.12257/) (2022)
- [Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images](https://mlsanity.com/paper/2204.10776/) (2022)
- [ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation](https://mlsanity.com/paper/2203.09418/) (2022)
- [OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models](https://mlsanity.com/paper/2301.07673/) (2023)
- [HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects](https://mlsanity.com/paper/1904.03167/) (2019)
- [Category Level Object Pose Estimation via Neural Analysis-by-Synthesis](https://mlsanity.com/paper/2008.08145/) (2020)

## Built on by (most cited first, 30 of 33)

- [Deep Learning-Based Object Pose Estimation: A Comprehensive Survey](https://mlsanity.com/paper/2405.07801/) (2024)
- [BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimation](https://mlsanity.com/paper/2504.02812/) (2025)
- [Co-op: Correspondence-based Novel Object Pose Estimation](https://mlsanity.com/paper/2503.17731/) (2025)
- [Accurate and efficient zero-shot 6D pose estimation with frozen foundation models](https://mlsanity.com/paper/2506.09784/) (2025)
- [Object Pose Estimation via the Aggregation of Diffusion Features](https://mlsanity.com/paper/2403.18791/) (2024)
- [Scalable Unseen Objects 6-DoF Absolute Pose Estimation with Robotic Integration](https://mlsanity.com/paper/2503.05578/) (2025)
- [GoTrack: Generic 6DoF Object Pose Refinement and Tracking](https://mlsanity.com/paper/2506.07155/) (2025)
- [PicoPose: Progressive Pixel-to-Pixel Correspondence Learning for Novel Object Pose Estimation](https://mlsanity.com/paper/2504.02617/) (2025)
- [RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation](https://mlsanity.com/paper/2510.18521/) (2025)
- [Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation](https://mlsanity.com/paper/2408.08234/) (2024)
- [BOP-Distrib: Revisiting 6D Pose Estimation Benchmarks for Better Evaluation under Visual Ambiguities](https://mlsanity.com/paper/2408.17297/) (2024)
- [Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Frames](https://mlsanity.com/paper/2503.03726/) (2025)
- [Foundation Feature-Driven Online End-Effector Pose Estimation: A Marker-Free and Learning-Free Approach](https://mlsanity.com/paper/2503.14051/) (2025)
- [SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow](https://mlsanity.com/paper/2504.09160/) (2025)
- [RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objects](https://mlsanity.com/paper/2505.10841/) (2025)
- [MR6D: Benchmarking 6D Pose Estimation for Mobile Robots](https://mlsanity.com/paper/2508.13775/) (2025)
- [OPFormer: Object Pose Estimation leveraging foundation model with geometric encoding](https://mlsanity.com/paper/2511.12614/) (2025)
- [Finding NeMO: A Geometry-Aware Representation of Template Views for Few-Shot Perception](https://mlsanity.com/paper/2602.04343/) (2026)
- [COG: Confidence-aware Optimal Geometric Correspondence for Unsupervised Single-reference Novel Object Pose Estimation](https://mlsanity.com/paper/2603.00493/) (2026)
- [EgoXtreme: A Dataset for Robust Object Pose Estimation in Egocentric Views under Extreme Conditions](https://mlsanity.com/paper/2603.25135/) (2026)
- [DLTPose: 6DoF Pose Estimation From Accurate Dense Surface Point Estimates](https://mlsanity.com/paper/2504.07335/) (2025)
- [Color-Pair Guided Robust Zero-Shot 6D Pose Estimation and Tracking of Cluttered Objects on Edge Devices](https://mlsanity.com/paper/2509.23647/) (2025)
- [Geo6DPose: Fast Zero-Shot 6D Object Pose Estimation via Geometry-Filtered Feature Matching](https://mlsanity.com/paper/2512.10674/) (2025)
- [PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoning](https://mlsanity.com/paper/2512.10840/) (2025)
- [MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation](https://mlsanity.com/paper/2601.06883/) (2026)
- [RHINO: Reconstructing Human Interactions with Novel Objects from Monocular Videos](https://mlsanity.com/paper/2605.17014/) (2026)
- [Object Pose and Shape Estimation for Grasping: Does it Work?](https://mlsanity.com/paper/2605.26944/) (2026)
- [Pose Anything Anywhere:Model-free Object Poses from Arbitrary References](https://mlsanity.com/paper/2606.23634/) (2026)
- [PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects](https://mlsanity.com/paper/2607.16015/) (2026)
- [RRTrack: Robust and Recoverable Object 6D Pose Tracking for Dynamic Scenes](https://mlsanity.com/paper/2607.23669/) (2026)

## Abstract (arXiv)

Despite the progress of learning-based methods for 6D object pose estimation, the trade-off between accuracy and scalability for novel objects still exists. Specifically, previous methods for novel objects do not make good use of the target object's 3D shape information since they focus on generalization by processing the shape indirectly, making them less effective. We present GenFlow, an approach that enables both accuracy and generalization to novel objects with the guidance of the target object's shape. Our method predicts optical flow between the rendered image and the observed image and refines the 6D pose iteratively. It boosts the performance by a constraint of the 3D shape and the generalizable geometric knowledge learned from an end-to-end differentiable system. We further improve our model by designing a cascade network architecture to exploit the multi-scale correlations and coarse-to-fine refinement. GenFlow ranked first on the unseen object pose estimation benchmarks in both the RGB and RGB-D cases. It also achieves performance competitive with existing state-of-the-art methods for the seen object pose estimation without any fine-tuning.