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REVIEW 4 major objections 6 minor 47 references

RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objects

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that a reference image and its geometry are enough to guide accurate 6D pose estimation of previously unseen objects, reporting a mean average recall of 61.4 across the seven BOP benchmark datasets.

desk verdict RefPose reports a modest but plausible SOTA on BOP unseen-object pose estimation, with a novel flow-based template selection and attention combination, but lacks error bars, code, and direct validation of its RAFT bottleneck. read the letter →

arxiv 2505.10841 v1 pith:SWOGBVDI submitted 2025-05-16 cs.CV

Jaeguk Kim , Jaewoo Park , Keuntek Lee , Nam Ik Cho This is my paper · ORCID
classification cs.CV
keywords 6Dposeestimationunseenobjectsgeometriccorrespondencerender-and-compareopticalflowcorrelationvolume-guidedattentiontemplateselectionBOPbenchmark
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that accurate 6D pose estimation for objects never seen during training is achievable from a single RGB image by using a reference image and its geometric correspondence as explicit guidance, rather than relying on shape priors learned from a fixed training set. RefPose first selects a few rendered templates, warps their known 3D geometry onto the query with optical flow, and votes per pixel to obtain a coarse pose. It then renders a reference from that pose, estimates the query's dense 3D correspondence through a U-Net with correlation-volume-guided attention, and repeatedly re-renders to refine the pose. If correct, this would let robots and augmented-reality systems handle novel objects without per-object training, and the reported experiments put the final average recall at 61.4 with a runtime of 3.9 seconds.

What carries the argument

The load-bearing object is the correlation volume of the optical-flow network (RAFT, a recurrent all-pairs optical-flow network), repurposed as an explicit pixel-wise attention map between query and reference images. Because query and reference depict the same object from nearby viewpoints, the softmax of this volume tells the geometry-estimation U-Net which reference pixels correspond to each query pixel, letting it fetch the correct values from the reference geometry. A second component is medoid voting in the warping stage: instead of averaging candidate 3D points from several warped templates, the method picks the most representative point per pixel, rejecting outlier flows. A third component is render-and-compare: the estimated query geometry stays fixed while the reference is re-rendered from the updated pose, and a CNN regresses the relative pose between the two geometries, repeated five times.

What would settle it

Measure how often the pixel-matching predictions are wrong between a photo and a rendered view for textureless or symmetric objects, and compare that error with RefPose's per-object pose error: if objects with high matching error still yield low pose error, the claimed dependence on optical flow is not the active mechanism; if high matching error tracks high pose error, the central premise is confirmed and the method's scope is limited to objects where pixel matching is reliable.

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Extended reading notes

Core claim

The central claim is that a two-stage pipeline built around optical-flow-based geometric correspondence can outperform existing unseen-object pose estimators on the BOP benchmark while keeping runtime competitive. In the first stage, a classifier scores pre-rendered templates by how reliably optical flow can be estimated between each template and the query, and the top-k templates are warped and combined through medoid voting to give an initial pose. In the second stage, that pose renders one well-aligned reference image with positionally encoded geometry, and a U-Net estimates the query geometry using attention weights taken directly from the correlation volume of the optical-flow network. The pose is then refined iteratively by a render-and-compare relative-pose estimator. In the paper's Table 1, this reaches a mean average recall of 61.4 across seven datasets, with the best scores on YCB-V, TUD-L, IC-BIN, and HB, while running in 3.9 seconds.

Load-bearing premise

The method's accuracy rests on the ability of a pretrained pixel-matching network to correctly map a real photo onto rendered images of the same object; on shiny, textureless, or symmetric objects that mapping is ambiguous, and no later step corrects it.

Editorial extensions

If this is right

  • If RefPose is correct, unseen-object pose estimation no longer requires a shape-prior network trained on the target object; one RGB image and the object's 3D model suffice.
  • A coarse pose good enough to render a well-aligned reference is sufficient to bootstrap a much more accurate final pose, shifting the burden from pose regression to correspondence quality.
  • The same optical-flow features serve template ranking, warping, and attention, so the pipeline avoids training a separate feature extractor and stays internally consistent.
  • The refinement stage transfers to coarse poses produced by other estimators, and the coarse stage benefits from other refiners, indicating the two stages are modular.
  • Because the method uses only RGB images and 3D models, it applies directly to settings without depth sensors.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: the method's dependence on optical flow suggests it will be weakest on textureless, specular, or rotationally symmetric objects where pixel correspondence is ambiguous; the lower scores on ITODD and LM-O in Table 1 are consistent with this reading.
  • Inference: because the attention weights come from the correlation volume rather than learned attention, the geometry network may require less data to generalize to new objects, which a smaller-training-set experiment could test directly.
  • Inference: the medoid-voting scheme implies robustness that should scale with the number of templates up to a point; an adaptive per-object choice of the number of selected templates, based on flow confidence, may improve the coarse stage further.
  • Inference: a direct comparison of per-object optical-flow error against per-object pose error would reveal whether flow quality, rather than pose regression, is the true bottleneck of the pipeline.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. RefPose is a two-stage monocular RGB 6D pose estimation method for unseen objects. In the first stage, it selects k templates from a set of N pre-rendered templates using a classifier trained to predict whether optical flow between a template and the query image will be accurate; it then warps the selected template geometries with RAFT optical flow, fuses the candidates by medoid-based pixel-wise voting, and obtains a coarse pose with PnP/RANSAC. In the second stage, it renders a reference image and a positionally encoded reference geometry from the coarse pose, estimates the query geometry with a U-Net whose attention weights come from the RAFT correlation volume, and iteratively refines the pose through a render-and-compare relative-pose estimator. The method is trained on Google Scanned Objects and evaluated on seven BOP datasets, where it reports a mean AR of 61.4 after refinement, the highest mean in Table 1, with a runtime of 3.9 seconds.

Significance. If the reported numbers are robust, RefPose is a meaningful advance for unseen-object pose estimation: it shows that a reference image together with geometric-correspondence guidance can reduce dependence on object-specific shape priors, and it integrates a standard optical-flow backbone into template selection, correspondence estimation, and refinement in a coherent way. The paper's strengths include the use of the standard BOP evaluation protocol, the component-level ablations in Tables 2 to 5, and the cross-method combination experiments in Table 1, which make it possible to attribute gains to the coarse pose and refinement modules separately. The attention visualization in Figure 7 is also useful evidence for the proposed correlation-volume-guided mechanism. The significance is conditional, however, because the manuscript provides no statistical error bars, no direct validation of the optical-flow backbone on BOP objects, and no code release, so the main SOTA claim rests on a small mean-AR margin and on an unstated empirical premise about flow quality.

major comments (4)
  1. [Section 4.2, Table 1] The central claim that RefPose achieves the best performance across all datasets is not supported with statistical evidence. The mean-AR advantage over GigaPose with GenFlow multi-hypothesis refinement is only 0.9 points (61.4 vs. 60.5), while on LM-O, T-LESS, and ITODD RefPose is actually behind that competitor (59.6 vs. 63.1, 57.8 vs. 58.2, and 43.8 vs. 45.3, respectively). Since only a single number is reported per method and dataset, there is no way to know whether these differences are within run-to-run noise. Please report standard deviations over multiple runs, or at minimum state the BOP evaluation convention and soften the claim to 'best mean AR' rather than 'best performance across all datasets.'
  2. [Section 4.1, classifier training] The label-generation procedure for the template-selection classifier is underspecified. The text says that positive and negative pairs are identified by comparing the predicted flow with the ground truth flow, but it does not give the threshold or matching criterion, the definition of ground-truth flow for a real query image, the occlusion/background handling, or the rendering pipeline used to create the GSO training pairs. Since template selection is the first stage of the pipeline and one of the paper's stated contributions, this omission makes the method non-reproducible and prevents a direct test of the premise that flow accuracy is a reliable template-selection signal.
  3. [Section 4.3, Tables 2 and 3] The hyperparameters N=128 and k=4 appear to be selected using AR computed on the same BOP test benchmarks that are later used for the final comparison. If these values were chosen after inspecting test-set performance, the reported numbers are optimistic and the comparison with methods whose hyperparameters were not tuned on the same test split is unfair. Please evaluate on a held-out BOP validation split, or provide evidence that the chosen values are stable and not test-set-selected.
  4. [Sections 3.2 and 3.3] The entire pipeline depends on the accuracy of the RAFT optical-flow network fine-tuned on GSO, but the manuscript contains no direct measurement of flow quality on BOP objects and no ablation that varies the flow source. Template selection (Section 3.2), warping-based geometry estimation (Section 3.2), and correlation-volume-guided attention (Section 3.3) all consume the same flow predictions, so errors in flow propagate to every stage. The pattern in Table 1, with weaker results on the textureless and industrial T-LESS and ITODD sets, is consistent with this bottleneck. Please add either an end-point-error style evaluation of the flow on BOP renderings or an ablation that replaces RAFT with a different correspondence estimator, so that the method is not tied to a single unvalidated backbone.
minor comments (6)
  1. [Table 1] In the GenFlow coarse row, the HB column reads '23. 3.8s', which appears to be missing a digit or has a formatting error; please fix.
  2. [Section 4.2] The text says 'our method demonstrates the best performance across all datasets' but also notes that it underperforms on LM-O, T-LESS, and ITODD; rephrase to 'best mean AR' to avoid an internal contradiction.
  3. [Section 4.1] The description of RAFT fine-tuning on GSO does not specify how training crops are generated (object scale, aspect ratio, background type, number of views), which complicates replication; please add these details.
  4. [Section 3.3, Eqs. (2) and (3)] The values of the sequence-loss weighting factor gamma and the exact pose-loss weighting between the two terms in Eq. (3) are not stated; please give the numerical values used in the experiments.
  5. [Section 3.2] The comparison with PFA's aggregation is qualitative; a precise description of how medoid voting differs from averaging over the candidate set in the presence of outliers would make the contribution clearer.
  6. [Figure 6] The qualitative comparison would be more useful if the predicted and ground-truth contours were annotated with per-image errors; as printed, the visual differences are hard to verify.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SOTA claim is anchored to external BOP benchmark ground truth and independent baselines; self-citations are component choices, not load-bearing.

full rationale

The paper's central claim (Table 1: RefPose reaches 61.4 mean AR on seven BOP datasets) is measured against external benchmark ground truth and independent baselines, so it is not a prediction derived from the method's own fitted values. The coarse-pose stage uses optical-flow warping plus PnP/RANSAC, and the refinement stage uses a geometry network trained with L1 loss against ground-truth geometry and a relative-pose estimator trained with pose losses; Eqs. (1)-(3) are standard supervised losses and do not reduce to the method's own outputs. The template classifier is trained with ground-truth flow labels, and no parameter is fitted to the reported test set and then renamed a prediction. The paper cites the authors' prior work ([33] for grid losses, [34] for positional encoding), but these are component choices, each ablated (e.g., Table 5 shows w/o P.E. 59.1 vs 61.4), and they do not carry the SOTA claim. Concerns about optical-flow reliability on textureless objects are correctness/robustness risks, not circularity. The derivation is therefore self-contained against the external benchmark.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on standard machine-learning training plus several hand-picked hyperparameters (N, k, M, Nfreq) and on the empirical transferability of RAFT optical flow to unseen objects. No parameter is fitted to the BOP test labels, so the circularity burden is low. The main unverified load-bearing element is the flow generalization assumption.

free parameters (5)
  • Number of pre-rendered templates N = 128
    Chosen by ablation on BOP (Table 2); 256 gives negligible gain (38.3 vs 38.1) at higher memory/cost.
  • Number of selected templates k = 4
    Chosen by ablation (Table 3); k=4 balances diversity and alignment, while k=8 worsens mean coarse AR from 38.1 to 36.2.
  • Pose refinement iterations M = 5
    Set to balance accuracy and runtime; no ablation for M is shown, so this is a hand-picked constant.
  • Positional encoding frequency bands Nfreq = 5
    Following the authors' prior work [34]; no ablation on Nfreq is reported.
  • Sequence loss weighting gamma = not specified
    Equation 2 uses an exponential weighting factor gamma, but its value is not reported.
assumptions (4)
  • domain assumption Pretrained RAFT optical flow, fine-tuned on GSO, transfers to BOP 'unseen' objects and yields reliable correspondences for template selection, warping, and attention.
    The entire pipeline is built on optical flow (classifier, warped geometry, correlation volume). No separate validation of flow quality on textureless or symmetric BOP objects is provided; Section 4.1 only states RAFT is fine-tuned on GSO.
  • domain assumption The object's 3D model is available at test time and can be rendered to produce templates and references.
    This is part of the BOP unseen-object setting (CAD models provided), and RefPose relies on rendering from the model at every stage; the method would not apply to model-free settings. See Section 3.2.
  • domain assumption Ground-truth optical flow can be generated between rendered template views and query views for GSO training, and flow-error labels are a sufficient criterion for selecting useful templates.
    Section 3.2 describes labels from comparing predicted and GT flow; the validity of this proxy for template usefulness is assumed.
  • domain assumption Training exclusively on GSO, excluding ShapeNet, suffices for BOP generalization.
    Section 4.1; the authors cite [15] to justify the choice, but no comparison with full MegaPose-style training is shown.

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Cite this review

Pith. "Pith review of RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objects." pith.science (2026). https://pith.science/paper/SWOGBVDI

@misc{pith2026250510841,
  author       = {Pith},
  title        = {Pith review of: RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objects},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SWOGBVDI}},
  note         = {Machine review of arXiv:2505.10841}
}
read the original abstract

Estimating the 6D pose of unseen objects from monocular RGB images remains a challenging problem, especially due to the lack of prior object-specific knowledge. To tackle this issue, we propose RefPose, an innovative approach to object pose estimation that leverages a reference image and geometric correspondence as guidance. RefPose first predicts an initial pose by using object templates to render the reference image and establish the geometric correspondence needed for the refinement stage. During the refinement stage, RefPose estimates the geometric correspondence of the query based on the generated references and iteratively refines the pose through a render-and-compare approach. To enhance this estimation, we introduce a correlation volume-guided attention mechanism that effectively captures correlations between the query and reference images. Unlike traditional methods that depend on pre-defined object models, RefPose dynamically adapts to new object shapes by leveraging a reference image and geometric correspondence. This results in robust performance across previously unseen objects. Extensive evaluation on the BOP benchmark datasets shows that RefPose achieves state-of-the-art results while maintaining a competitive runtime.

Figures

Figures reproduced from arXiv: 2505.10841 by the authors.

Figure 1
Figure 1. Comparison between conventional methods and pro [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the RefPose pipeline. Given an input RGB image, the target object is first detected, cropped, and resized to create [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Warping-based geometry estimation process. The opti [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Architecture of the geometry estimation network. The [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Qualitative comparison of pose estimation results. We [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Visual comparison of attention mechanisms in the geom [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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Reference graph

Works this paper leans on

47 extracted references · 34 canonical work pages

  1. [1]

    Zs6d: Zero-shot 6d object pose estimation using vision transformers

    Philipp Ausserlechner, David Haberger, Stefan Thalhammer, Jean-Baptiste Weibel, and Markus Vincze. Zs6d: Zero-shot 6d object pose estimation using vision transformers. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 463–469. IEEE, 2024

    2024

  2. [2]

    A stereo vision approach for cooperative robotic movement therapy

    Benjamin Busam, Marco Esposito, Simon Che’Rose, Nassir Navab, and Benjamin Frisch. A stereo vision approach for cooperative robotic movement therapy. In Proceedings of the IEEE international conference on computer vision work- shops, pages 127–135, 2015

    work page 2015

  3. [3]

    Reconstruct locally, localize glob- ally: A model free method for object pose estimation

    Ming Cai and Ian Reid. Reconstruct locally, localize glob- ally: A model free method for object pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 3153–3163, 2020

    work page 2020

  4. [4]

    Shapenet: An information-rich 3d model repository

    Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015

    arXiv 2015

  5. [5]

    Multi-view 3d object detection network for autonomous driving

    Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia. Multi-view 3d object detection network for autonomous driving. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 1907–1915, 2017

    1907

  6. [6]

    Category level object pose estimation via neu- ral analysis-by-synthesis

    Xu Chen, Zijian Dong, Jie Song, Andreas Geiger, and Ot- mar Hilliges. Category level object pose estimation via neu- ral analysis-by-synthesis. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVI 16, pages 139–156. Springer, 2020

    work page 2020

  7. [7]

    So-pose: Exploiting self- occlusion for direct 6d pose estimation

    Yan Di, Fabian Manhardt, Gu Wang, Xiangyang Ji, Nassir Navab, and Federico Tombari. So-pose: Exploiting self- occlusion for direct 6d pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 12396–12405, 2021

    work page 2021

  8. [8]

    Flownet: Learning optical flow with convolutional networks

    Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox. Flownet: Learning optical flow with convolutional networks. In Pro- ceedings of the IEEE international conference on computer vision, pages 2758–2766, 2015

    2015

Show all 47 references
  1. [9]

    Google scanned objects: A high- quality dataset of 3d scanned household items

    Laura Downs, Anthony Francis, Nate Koenig, Brandon Kin- man, Ryan Hickman, Krista Reymann, Thomas B McHugh, and Vincent Vanhoucke. Google scanned objects: A high- quality dataset of 3d scanned household items. In 2022 In- ternational Conference on Robotics and Automation (ICRA...

    2022

  2. [10]

    Shape- constraint recurrent flow for 6d object pose estimation

    Yang Hai, Rui Song, Jiaojiao Li, and Yinlin Hu. Shape- constraint recurrent flow for 6d object pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 4831–4840, 2023

    2023

  3. [11]

    Epos: Esti- mating 6d pose of objects with symmetries

    Tomas Hodan, Daniel Barath, and Jiri Matas. Epos: Esti- mating 6d pose of objects with symmetries. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11703–11712, 2020

    2020

  4. [12]

    Bop challenge 2023 on detection segmentation and pose estimation of seen and unseen rigid objects

    Tomas Hodan, Martin Sundermeyer, Yann Labbe, Van Nguyen Nguyen, Gu Wang, Eric Brachmann, Bertram Drost, Vincent Lepetit, Carsten Rother, and Jiri Matas. Bop challenge 2023 on detection segmentation and pose estimation of seen and unseen rigid objects. In Proceedings of the IEE...

    2023

  5. [13]

    Perspective flow aggregation for data-limited 6d object pose estimation

    Yinlin Hu, Pascal Fua, and Mathieu Salzmann. Perspective flow aggregation for data-limited 6d object pose estimation. In European Conference on Computer Vision, pages 89–106. Springer, 2022

    2022

  6. [14]

    Cosypose: Consistent multi-view multi-object 6d pose estimation

    Yann Labb ´e, Justin Carpentier, Mathieu Aubry, and Josef Sivic. Cosypose: Consistent multi-view multi-object 6d pose estimation. In Computer Vision–ECCV 2020: 16th Euro- pean Conference, Glasgow, UK, August 23–28, 2020, Pro- ceedings, Part XVII 16, pages 574–591. Springer, 2020

    2020

  7. [15]

    Megapose: 6d pose estimation of novel objects via render & compare.arXiv preprint arXiv:2212.06870, 2022

    Yann Labb ´e, Lucas Manuelli, Arsalan Mousavian, Stephen Tyree, Stan Birchfield, Jonathan Tremblay, Justin Carpentier, Mathieu Aubry, Dieter Fox, and Josef Sivic. Megapose: 6d pose estimation of novel objects via render & compare.arXiv preprint arXiv:2212.06870, 2022

    2022 arXiv

  8. [16]

    Ep n p: An accurate o (n) solution to the p n p problem

    Vincent Lepetit, Francesc Moreno-Noguer, and Pascal Fua. Ep n p: An accurate o (n) solution to the p n p problem. International journal of computer vision, 81:155–166, 2009

    2009

  9. [17]

    Polarmesh: A star-convex 3d shape approximation for object pose estimation

    Fu Li, Ivan Shugurov, Benjamin Busam, Minglong Li, Shaowu Yang, and Slobodan Ilic. Polarmesh: A star-convex 3d shape approximation for object pose estimation. IEEE Robotics and Automation Letters, 7(2):4416–4423, 2022

    2022

  10. [18]

    Deepim: Deep iterative matching for 6d pose estimation

    Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, and Dieter Fox. Deepim: Deep iterative matching for 6d pose estimation. In Proceedings of the European Conference on Computer Vi- sion (ECCV), pages 683–698, 2018

    2018

  11. [19]

    Cdpn: Coordinates-based disentangled pose network for real-time rgb-based 6-dof object pose estimation

    Zhigang Li, Gu Wang, and Xiangyang Ji. Cdpn: Coordinates-based disentangled pose network for real-time rgb-based 6-dof object pose estimation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7678–7687, 2019

    2019

  12. [20]

    Checkerpose: Progressive dense keypoint localization for object pose estimation with graph neural network

    Ruyi Lian and Haibin Ling. Checkerpose: Progressive dense keypoint localization for object pose estimation with graph neural network. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 14022–14033, 2023

    2023

  13. [21]

    Decoupled weight decay regularization

    I Loshchilov. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017

    2017 arXiv

  14. [22]

    Sgdr: Stochas- tic gradient descent with warm restarts

    Ilya Loshchilov and Frank Hutter. Sgdr: Stochas- tic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016

    2016 arXiv

  15. [23]

    Roi- 10d: Monocular lifting of 2d detection to 6d pose and met- ric shape

    Fabian Manhardt, Wadim Kehl, and Adrien Gaidon. Roi- 10d: Monocular lifting of 2d detection to 6d pose and met- ric shape. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 2069– 2078, 2019

    2019

  16. [24]

    Cps++: Improving class-level 6d pose and shape estimation from monocular images with self- supervised learning

    Fabian Manhardt, Gu Wang, Benjamin Busam, Manuel Nickel, Sven Meier, Luca Minciullo, Xiangyang Ji, and Nassir Navab. Cps++: Improving class-level 6d pose and shape estimation from monocular images with self- supervised learning. arXiv preprint arXiv:2003.05848, 2020

    2003 arXiv

  17. [25]

    Pose estimation for augmented reality: a hands-on survey

    Eric Marchand, Hideaki Uchiyama, and Fabien Spindler. Pose estimation for augmented reality: a hands-on survey. IEEE transactions on visualization and computer graphics , 22(12):2633–2651, 2015

    2015

  18. [26]

    A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation

    Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and ...

    2016

  19. [27]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM, 65(1):99–106, 2021

    2021

  20. [28]

    Genflow: Generalizable recurrent flow for 6d pose refinement of novel objects

    Sungphill Moon, Hyeontae Son, Dongcheol Hur, and Sang- wook Kim. Genflow: Generalizable recurrent flow for 6d pose refinement of novel objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10039–10049, 2024

    2024

  21. [29]

    Cnos: A strong base- line for cad-based novel object segmentation

    Van Nguyen Nguyen, Thibault Groueix, Georgy Ponimatkin, Vincent Lepetit, and Tomas Hodan. Cnos: A strong base- line for cad-based novel object segmentation. InProceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 2134–2140, 2023

    2023

  22. [30]

    Gigapose: Fast and robust novel object pose estimation via one correspondence

    Van Nguyen Nguyen, Thibault Groueix, Mathieu Salzmann, and Vincent Lepetit. Gigapose: Fast and robust novel object pose estimation via one correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9903–9913, 2024

    2024

  23. [31]

    Dinov2: Learning robust visual features without supervision

    Maxime Oquab, Timoth ´ee Darcet, Th ´eo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023

    2023 arXiv

  24. [32]

    Found- pose: Unseen object pose estimation with foundation fea- tures

    Evin Pınar ¨Ornek, Yann Labb ´e, Bugra Tekin, Lingni Ma, Cem Keskin, Christian Forster, and Tomas Hodan. Found- pose: Unseen object pose estimation with foundation fea- tures. In European Conference on Computer Vision , pages 163–182. Springer, 2025

    2025

  25. [33]

    Dprost: Dynamic projec- tive spatial transformer network for 6d pose estimation

    Jaewoo Park and Nam Ik Cho. Dprost: Dynamic projec- tive spatial transformer network for 6d pose estimation. In European Conference on Computer Vision, pages 363–379. Springer, 2022

    2022

  26. [34]

    Leveraging positional encoding for robust multi-reference-based object 6d pose estimation

    Jaewoo Park, Jaeguk Kim, and Nam Ik Cho. Leveraging positional encoding for robust multi-reference-based object 6d pose estimation. arXiv preprint arXiv:2401.16284, 2024

    2024 arXiv

  27. [35]

    Pix2pose: Pixel-wise coordinate regression of objects for 6d pose esti- mation

    Kiru Park, Timothy Patten, and Markus Vincze. Pix2pose: Pixel-wise coordinate regression of objects for 6d pose esti- mation. In Proceedings of the IEEE/CVF international con- ference on computer vision, pages 7668–7677, 2019

    2019

  28. [36]

    Robot guidance using ma- chine vision techniques in industrial environments: A com- parative review

    Luis P ´erez, ´I˜nigo Rodr´ıguez, Nuria Rodr´ıguez, Rub´en Usa- mentiaga, and Daniel F Garc ´ıa. Robot guidance using ma- chine vision techniques in industrial environments: A com- parative review. Sensors, 16(3):335, 2016

    2016

  29. [37]

    Bb8: A scalable, accu- rate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth

    Mahdi Rad and Vincent Lepetit. Bb8: A scalable, accu- rate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth. In Pro- ceedings of the IEEE international conference on computer vision, pages 3828–3836, 2017

    2017

  30. [38]

    6dof object tracking based on 3d scans for augmented reality remote live support

    Jason Rambach, Alain Pagani, Michael Schneider, Olek- sandr Artemenko, and Didier Stricker. 6dof object tracking based on 3d scans for augmented reality remote live support. Computers, 7(1):6, 2018

    2018

  31. [39]

    U- net: Convolutional networks for biomedical image segmen- tation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- net: Convolutional networks for biomedical image segmen- tation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, pa...

    2015

  32. [40]

    Binary cross entropy with deep learning technique for image classification

    Usha Ruby and Vamsidhar Yendapalli. Binary cross entropy with deep learning technique for image classification. Int. J. Adv. Trends Comput. Sci. Eng, 9(10), 2020

    2020

  33. [41]

    Osop: A multi-stage one shot object pose estimation frame- work

    Ivan Shugurov, Fu Li, Benjamin Busam, and Slobodan Ilic. Osop: A multi-stage one shot object pose estimation frame- work. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 6835–6844, 2022

    2022

  34. [42]

    Zebrapose: Coarse to fine surface encod- ing for 6dof object pose estimation

    Yongzhi Su, Mahdi Saleh, Torben Fetzer, Jason Rambach, Nassir Navab, Benjamin Busam, Didier Stricker, and Fed- erico Tombari. Zebrapose: Coarse to fine surface encod- ing for 6dof object pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ...

    2022

  35. [43]

    Raft: Recurrent all-pairs field transforms for optical flow

    Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23– 28, 2020, Proceedings, Part II 16, pages 402–419. Springer, 2020

    2020

  36. [44]

    Real-time seamless single shot 6d object pose prediction

    Bugra Tekin, Sudipta N Sinha, and Pascal Fua. Real-time seamless single shot 6d object pose prediction. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 292–301, 2018

    2018

  37. [45]

    Gdr-net: Geometry-guided direct regression net- work for monocular 6d object pose estimation

    Gu Wang, Fabian Manhardt, Federico Tombari, and Xi- angyang Ji. Gdr-net: Geometry-guided direct regression net- work for monocular 6d object pose estimation. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16611–16621, 2021

    2021

  38. [46]

    Normalized object coordinate space for category-level 6d object pose and size estimation

    He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, and Leonidas J Guibas. Normalized object coordinate space for category-level 6d object pose and size estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages ...

    2019

  39. [47]

    Dpod: 6d pose object detector and refiner

    Sergey Zakharov, Ivan Shugurov, and Slobodan Ilic. Dpod: 6d pose object detector and refiner. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1941–1950, 2019

    1941

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.