MR6D: Benchmarking 6D Pose Estimation for Mobile Robots

Alice Kirchheim; Anas Gouda; Christian Blesing; Frank Hoffmann; Lokeshwaran Manohar; Shrutarv Awasthi

REVIEW 3 major objections 5 minor 29 references

MR6D: Benchmarking 6D Pose Estimation for Mobile Robots

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read MR6D introduces a benchmark that exposes the gap between current 6D pose estimators and mobile-robot perception needs.

desk verdict MR6D is a genuinely useful dataset for mobile-robot 6D pose estimation, with the main caveat that ground-truth annotation accuracy is asserted rather than measured. read the letter →

arxiv 2508.13775 v1 pith:PACK56HW submitted 2025-08-19 cs.CV cs.RO

Anas Gouda , Shrutarv Awasthi , Christian Blesing , Lokeshwaran Manohar , Frank Hoffmann , Alice Kirchheim This is my paper · ORCID

classification cs.CVcs.RO

keywords 6Dposeestimationmobileroboticsbenchmarkdatasetunseenobjectindustrialobjectslong-rangeperceptionocclusionrobotic

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 introduces MR6D, a benchmark dataset for 6D object pose estimation aimed at mobile robots rather than fixed robot arms. It contains 92 real-world scenes with 16 industrial objects—pallets, storage bins, and consumer containers—captured from low, distant, and moving viewpoints that match how mobile platforms perceive their surroundings. The central claim is that these conditions are underrepresented in existing pose benchmarks, and the paper backs this by showing that current pose pipelines designed for unseen objects reach only modest accuracy on MR6D: about 0.35 average recall with ground-truth segmentation and about 0.18 when segmentation must be predicted as well. If the benchmark is sound, it gives mobile-robotics research a realistic testbed for a perception problem that household-object datasets do not exercise.

What carries the argument

The load-bearing artifact is the dataset itself, MR6D, with its annotation methodology. Four capture setups produce four subsets, and each subset's ground truth is built through a different chain: motion-capture tracking with eye-in-hand calibration for the static validation and dynamic sets; a learned multi-view reconstruction model plus odometry-derived scale, manually refined where odometry is noisy, for the low-mounted outdoor set; and fully manual scale alignment for the simulated-wheeled-robot set. Object meshes come either from manual design (the Euro pallet) or from a neural reconstruction method applied to high-accuracy depth images. These choices let the authors argue that the scenes are genuinely harder—larger objects, longer ranges, severe self-occlusion, sunlight-degraded depth—so the low reported accuracy reflects the gap between current algorithms and mobile-robot perception needs.

What would settle it

Measure the ground-truth poses against an independent high-accuracy reference, such as a laser tracker or a second motion-capture system, on a sample of MR6D scenes—especially the static subsets; if typical errors exceed the stated low-centimeter bound by a wide margin, the reported underperformance could be an artifact of annotation noise rather than genuine algorithm limits.

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Extended reading notes

Core claim

MR6D's core discovery is that mobile-robotic conditions form a distinct evaluation regime in which current pose estimators generalize poorly. The dataset spans four capture setups: a static validation set and a dynamic set with both camera and object motion tracked by a multi-camera motion capture system; an indoor-outdoor set filmed from a low-mounted robot camera at roughly 40 cm height, with camera trajectories recovered by a feed-forward multi-view reconstruction model and scale adjusted from odometry or manual alignment; and a set simulating a wheeled robot's approach to objects. On two unseen-object evaluation pipelines—one seeded with ground-truth segmentation masks and one fully automatic—the paper reports average recall scores of 0.3462 and 0.1841, and identifies misidentification under occlusion, closely stacked similar objects, and similar-textured faces as recurring failure modes.

Load-bearing premise

The benchmark's validity rests on ground-truth pose accuracy that is visually checked and expected to be within the low-centimeter range for static scenes, but never independently measured; dynamic scenes and manually scaled trajectories can carry larger unquantified errors.

Editorial extensions

If this is right

A mobile-robotics-specific pose benchmark now exists, so new methods can be compared on distance, viewpoint, object scale, and occlusion rather than only on household clutter.
Even with perfect segmentation, current unseen-object pose estimators score near 0.35 average recall, leaving substantial room for improvement on these conditions.
The gap between ground-truth-mask and fully automatic pipelines (0.3462 vs 0.1841) shows that better 2D segmentation directly transfers into pose accuracy.
Because the object meshes are released, seen-object pipelines can be trained on synthetic renderings, enabling a fair comparison between methods specialized to mobile robots and the provided baselines.
The identified failure modes—occlusion-induced misidentification, similar stacked objects, and similar-textured faces—point to concrete refinement targets.

Reading between the lines

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

A natural test of the distance hypothesis: splitting MR6D frames into distance bins and recomputing average recall would show whether long-range depth degradation, rather than viewpoint or scale per se, drives the low scores.
An independent metrological audit of the ground-truth poses, using a laser tracker or precisely dimensioned fiducials on a sample of scenes, would separate annotation noise from genuine algorithm failure.
The paper notes that Euro pallets deviate from nominal dimensions and that a few objects deform slightly; if mesh-based evaluation ignores these tolerances, reported scores may carry a systematic error that per-instance tolerances could absorb.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: MR6D is a real, usable dataset that fills a gap in 6D pose estimation benchmarks. If you work on mobile robotics perception, it's worth knowing about. The main caveat is that the ground-truth pose accuracy is never independently measured, which is the one issue I'd want addressed before relying on it.

What's new: the dataset targets large objects at long range, low camera perspectives, self-occlusion, and dynamic scenes with a moving camera and moving objects. That's a genuinely different regime than YCB-V, T-LESS, or the household-focused BOP datasets. They built 92 scenes with 16 objects, provide meshes (BundleSDF, manual for the pallet), and release it on HuggingFace. The annotation pipeline uses VICON where available, BOP toolkit for manual refinement, and VGGT plus manual scale for the odometry-only subsets. That is a reasonable recipe and they document it clearly.

The evaluation is honest but thin. They run FoundationPose with GT masks and with CTL masks, report BOP AR numbers per subset. The numbers are low (0.35 with GT masks, 0.18 with CTL masks), which supports the claim that mobile-robot settings are harder than the household benchmarks. What they don't do is run the same pipeline on an existing dataset like YCB-V or T-LESS under the same protocol, so the "underperform" claim is not actually controlled. That is a real gap, but it doesn't weaken the dataset itself.

The bigger soft spot is the ground-truth quality. Section 3.2 says static scenes are 'expected to be accurate within the low-centimeter range' based on visual checks, but there is no independent measurement against, say, a laser tracker or a CAD-based re-projection error audit. For long-range scenes, sub-degree angular errors turn into multi-centimeter errors at 2 meters, and they admit that some objects deform and pallets vary. The dynamic subset, with both camera and object tracked by VICON, is probably fine, but the O³dyn and MR-like subsets rely on manually scaled trajectories where the error could be larger. For a benchmark, this is the load-bearing assumption. It's not fatal, but it's worth pushing on: measure the error on a few scenes and report it.

The self-citation concern is minor. CTL is from the same group, but they compare it to DINOv2 and the benchmark value doesn't depend on CTL's success. Reusing MTevent objects is fine because the tasks don't overlap.

Bottom line: this is a useful resource for the mobile-robotics and pose-estimation community. It deserves serious peer review, with the request that the authors measure and report annotation error, and ideally add a controlled comparison on at least one existing dataset. I'd accept it with revisions.

Referee Report

3 major / 5 minor

Summary. The paper introduces MR6D, a dataset and benchmark for 6D pose estimation in mobile robotics, comprising 92 real-world scenes with 16 objects across static and dynamic subsets captured from low-mounted, long-range, and occluded perspectives. The authors evaluate two unseen-object pipelines on the dataset: FoundationPose with ground-truth masks and FoundationPose with CTL-generated masks, reporting BOP metrics (AR 0.3462 and 0.1841 on test subsets, respectively). The central claims are that MR6D fills a gap in mobile-robot-relevant pose estimation benchmarks and that current unseen-object pipelines underperform in these settings.

Significance. If the ground-truth annotations are trustworthy, MR6D addresses a genuine gap: existing BOP-style datasets focus on small household objects and arm-mounted cameras, whereas MR6D targets long-range, low-perspective, large-object, and occluded conditions relevant to industrial mobile robots. The dataset is publicly released, uses BOP-compatible format and metrics, and provides 3D meshes for all objects. The authors also identify segmentation as a bottleneck in fully unseen pipelines, which is a useful finding. However, the strength of these contributions depends on the accuracy of the pose annotations and on a controlled comparison to existing datasets, both of which are currently lacking or underdeveloped.

major comments (3)

[3.2, 'Quality of the annotations'] The central validity claim of the benchmark rests on unmeasured ground-truth accuracy. The paper states that 'Based on our annotation process and visual checks, we expect static scenes to be accurate within the low-centimeter range' without any independent reference measurement. The dynamic subset relies on VICON tracking with possible marker occlusions, and the O³dyn and MR-like subsets use VGGT trajectories whose scale is manually refined when odometry is noisy. Given that BOP metrics compare predictions directly against these annotations, unquantified errors—especially at long range where small angular errors translate into large translation errors—could account for the reported AR values. The authors should provide a quantitative annotation-error analysis, e.g., against an independent high-precision reference or via multi-annotator agreement, and report per-subset error bounds.
[4.1, Table 1] The claim that current 6D pipelines 'underperform' in mobile-robot settings is not supported by a controlled comparison. The paper reports AR only on MR6D; it does not run the same pipelines on established BOP datasets (e.g., YCB-V, T-LESS, ITODD) under identical protocols. Without such baselines, the absolute AR values (0.3462 with GT masks, 0.1841 with CTL masks) cannot be interpreted as evidence that the mobile-robot setting is especially challenging, as opposed to reflecting properties of the chosen methods or of the annotation quality.
[4.1, Section 2] The evaluation that underlies the 'underperform' conclusion uses only one pose estimator (FoundationPose) and one segmentation method (CTL). The paper's abstract and conclusion generalize to 'current 6D pipelines', but a single pipeline is insufficient to establish a systematic deficiency. At minimum, the authors should add at least one more unseen-object pose estimator (e.g., MegaPose or GigaPose) and one more segmentation method (e.g., CNOS), or explicitly state that the results hold only for the FoundationPose/CTL combination.

minor comments (5)

[Figure 2] The point labeled 'MR6D (All)' is the mean over subsets rather than a global per-scene average, which could mislead readers; the caption discloses this, but the figure itself should use distinct markers or a footnote.
[References [9] and [10]] References [9] and [10] cite the same BOP 2018 paper twice; please consolidate into a single reference or clearly differentiate the 2018 and 2020 BOP publications.
[Figure 9] The caption for Figure 9 says 'Quantitative results' but the figure shows qualitative visualizations of pose projections; the caption should read 'Qualitative results'.
[Section 3.1] The statement that the IKEA objects 'will remain available through at least the end of 2026' is a useful reproducibility detail, but it also underlines that the dataset's long-term availability may depend on commercial product lifecycles; consider noting this in the dataset documentation.
[Section 4.2] The sentence attributing poor results to 'poor initialization due to depth values for FoundationPose' is not substantiated by an experiment; either add evidence or soften the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: MR6D is a dataset/benchmark paper whose evaluations are empirical measurements, not derivations from fitted inputs or self-citations.

full rationale

The paper's central contribution is a new dataset and an evaluation of existing unseen-object 6D pose pipelines on it. No quantity is derived from an equation that is equivalent to its own input. The reported AR values in Table 1 are empirical results of running FoundationPose and CTL-based segmentation; they are not fitted parameters renamed as predictions. The use of CTL [5], a method by one of the authors, is a self-citation, but it is not load-bearing: the benchmark's validity does not depend on CTL's success, the paper also reports results with ground-truth masks, and the segmentation comparison is presented as a practical choice rather than as a theorem. The reuse of objects from MTevent [1] is similarly non-circular because MR6D provides new scenes, new annotations, and a different evaluation purpose. The ground-truth accuracy statement in Sec. 3.2 ('Based on our annotation process and visual checks, we expect static scenes to be accurate within the low-centimeter range') is an unverified limitation, but it is a correctness/validity concern, not a circular derivation. The O³dyn and MR-like subsets use manual scale refinement and manual pose annotation, which are standard practices rather than derivations from the models being evaluated. No uniqueness theorem, ansatz-smuggling citation, or renaming of a known result occurs. The derivation chain is therefore self-contained, and no circular step can be exhibited.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The dataset is an empirical artifact, so the central claim rests on the accuracy of the sensor and annotation pipeline rather than on free parameters in a model. The calibration parameters listed are the manually adjusted numbers that directly control ground-truth accuracy, and the axioms state the unverified accuracy assumptions of the motion-capture, reconstruction, and manual alignment steps.

free parameters (2)

Trajectory scale factor for O³dyn and MR-like subsets = Manually set per scene, values not reported
VGGT produces scale-free trajectories; odometry provides initial scale, with manual refinement via BOP tool when odometry is noisy. This directly sets the 3D coordinates of all annotations in these subsets.
Object calibration offsets (obj_MoCap to obj_geometry) = Per-object 6D offsets computed from VICON captures and manual alignment
The dynamic subset requires aligning the motion-capture marker frame to each object mesh; errors here propagate into the per-frame object poses.

assumptions (4)

domain assumption VICON motion capture provides accurate camera and object trajectories.
Used as ground truth in the validation and dynamic subsets; accuracy is not independently validated. Referenced in Section 3.2.
domain assumption VGGT with odometry scaling reconstructs correct camera trajectories for the O³dyn subset.
The paper acknowledges manual scale refinement is needed with noisy odometry, so this assumption is only partially trusted. Referenced in Section 3.2, O³dyn subset.
domain assumption BundleSDF object meshes are accurate enough for pose annotation and downstream pose estimation.
Most meshes come from BundleSDF; any reconstruction error limits the fidelity of pose comparisons. Referenced in Section 3.1.
domain assumption Manual annotations with the BOP tool are accurate and unbiased.
Every subset uses manual refinement steps; the paper supports accuracy with visual checks rather than measurement. Referenced in Section 3.2.

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Cite this review

Pith. "Pith review of MR6D: Benchmarking 6D Pose Estimation for Mobile Robots." pith.science (2026). https://pith.science/paper/PACK56HW

@misc{pith2026250813775,
  author       = {Pith},
  title        = {Pith review of: MR6D: Benchmarking 6D Pose Estimation for Mobile Robots},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PACK56HW}},
  note         = {Machine review of arXiv:2508.13775}
}

read the original abstract

Existing 6D pose estimation datasets primarily focus on small household objects typically handled by robot arm manipulators, limiting their relevance to mobile robotics. Mobile platforms often operate without manipulators, interact with larger objects, and face challenges such as long-range perception, heavy self-occlusion, and diverse camera perspectives. While recent models generalize well to unseen objects, evaluations remain confined to household-like settings that overlook these factors. We introduce MR6D, a dataset designed for 6D pose estimation for mobile robots in industrial environments. It includes 92 real-world scenes featuring 16 unique objects across static and dynamic interactions. MR6D captures the challenges specific to mobile platforms, including distant viewpoints, varied object configurations, larger object sizes, and complex occlusion/self-occlusion patterns. Initial experiments reveal that current 6D pipelines underperform in these settings, with 2D segmentation being another hurdle. MR6D establishes a foundation for developing and evaluating pose estimation methods tailored to the demands of mobile robotics. The dataset is available at https://huggingface.co/datasets/anas-gouda/mr6d.

Figures

Figures reproduced from arXiv: 2508.13775 by the authors.

**Figure 1.** Many mobile robot platforms use specialized gripping mechanisms rather than standard robotic arms, allowing them to handle [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Dataset Comparison: Average object distance from the camera and average object volume. Triangles indicate MR6D subsets. ”MR6D (all)” represents the mean across its subsets, rather than the global per-scene average. For other datasets, averages are computed over the validation or test subset, depending on public availability. while handling occlusions and extreme viewpoints, increasing pose estimation difficulty. Sp… view at source ↗

**Figure 3.** The objects used in our MR6D dataset. These objects [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 5.** Figure 5: Annotating object 6D pose in the validation subset. After [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

**Figure 6.** Figure 6: Dataset collection process for the validation and dy [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

**Figure 7.** Figure 7: Data statistics. The first plot shows object dimensions (length, breadth, height), highlighting the minimum, median, and maximum [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

**Figure 8.** Figure 8: MR6D comprises four subsets, each shown in a row: static validation scenes (Row 1), human-manipulated dynamic scenes [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 9.** Figure 9: Quantitative results for GT-masks + FoundationPose. Ground truth (GT) is shown in green, and FoundationPose results are [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]

**Figure 10.** Figure 10: Challenging cases in evaluation with GT-masks + FoundationPose. Ground truth (GT) is shown in green, and FoundationPose [PITH_FULL_IMAGE:figures/full_fig_p007_10.png]

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Works this paper leans on

29 extracted references · 25 canonical work pages

[1]

Mtevent: A multi-task event camera dataset for 6d pose estimation and moving object detection

Shrutarv Awasthi, Anas Gouda, Sven Franke, J ´erˆome Ruti- nowski, Frank Hoffmann, and Moritz Roidl. Mtevent: A multi-task event camera dataset for 6d pose estimation and moving object detection. In Proceedings of the Computer Vi- sion and Pattern Recognition Conference, pages 5063–5071,

work page
[2]

Intro- ducing hot3d: An egocentric dataset for 3d hand and object tracking

Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon, Shreyas Hampali, Fan Zhang, Jade Fountain, Edward Miller, Selen Basol, Richard Newcombe, Robert Wang, et al. Intro- ducing hot3d: An egocentric dataset for 3d hand and object tracking. arXiv preprint arXiv:2406.09598, 2024. 1

arXiv 2024
[3]

Strobl, Matthias Humt, and Rudolph Triebel

Maximilian Denninger, Dominik Winkelbauer, Martin Sun- dermeyer, Wout Boerdijk, Markus Knauer, Klaus H. Strobl, Matthias Humt, and Rudolph Triebel. Blenderproc2: A procedural pipeline for photorealistic rendering. Journal of Open Source Software, 8(82):4901, 2023. 4

work page 2023
[4]

Introducing mvtec itodd - a dataset for 3d object recognition in industry

Bertram Drost, Markus Ulrich, Paul Bergmann, Philipp Hartinger, and Carsten Steger. Introducing mvtec itodd - a dataset for 3d object recognition in industry. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2017. 1, 2

work page 2017
[5]

Learning embeddings with centroid triplet loss for object identification in robotic grasp- ing

Anas Gouda, Max Schwarz, Christopher Reining, Sven Behnke, and Alice Kirchheim. Learning embeddings with centroid triplet loss for object identification in robotic grasp- ing. In 2024 IEEE 20th International Conference on Au- tomation Science and Engineering (CASE) , pages 3577– 3583, 2024. 3, 6

work page 2024
[6]

Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions

Andrew Guo, Bowen Wen, Jianhe Yuan, Jonathan Tremblay, Stephen Tyree, Jeffrey Smith, and Stan Birchfield. Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 11428–11435, 2023. 1

work page 2023
[7]

Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes

Stefan Hinterstoisser, Vincent Lepetit, Slobodan Ilic, Ste- fan Holzer, Gary Bradski, Kurt Konolige, and Nassir Navab. Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In Com- puter vision–ACCV 2012: 11th asian conference on com- puter vision, daejeon, korea, november 5-9, 2012, revised selected p...

work page 2012
[8]

T-LESS: An RGB-D dataset for 6D pose estimation of texture-less ob- jects

Tom ´aˇs Hoda ˇn, Pavel Haluza, ˇStˇep´an Obdrˇz´alek, Jiˇr´ı Matas, Manolis Lourakis, and Xenophon Zabulis. T-LESS: An RGB-D dataset for 6D pose estimation of texture-less ob- jects. IEEE Winter Conference on Applications of Computer Vision (WACV), 2017. 1, 2

work page 2017

Show all 29 references

[10]

BOP: Benchmark for 6D object pose esti- mation

Tom ´aˇs Hodaˇn, Frank Michel, Eric Brachmann, Wadim Kehl, Anders Glent Buch, Dirk Kraft, Bertram Drost, Joel Vidal, Stephan Ihrke, Xenophon Zabulis, Caner Sahin, Fabian Man- hardt, Federico Tombari, Tae-Kyun Kim, Ji ˇr´ı Matas, and Carsten Rother. BOP: Benchmark for 6D object...
[11]

BOP challenge 2020 on 6D object localization

Tom ´aˇs Hoda ˇn, Martin Sundermeyer, Bertram Drost, Yann Labb´e, Eric Brachmann, Frank Michel, Carsten Rother, and Jiˇr´ı Matas. BOP challenge 2020 on 6D object localization. European Conference on Computer Vision Workshops (EC- CVW), 2020. 6

2020
[12]

Homebreweddb: Rgb-d dataset for 6d pose esti- mation of 3d objects

Roman Kaskman, Sergey Zakharov, Ivan Shugurov, and Slo- bodan Ilic. Homebreweddb: Rgb-d dataset for 6d pose esti- mation of 3d objects. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision (ICCV) Workshops,
[13]

evobot – design and learning-based control of a two-wheeled compound inverted pendulum robot

Patrick Klokowski, Julian Eßer, Nils Gramse, Benedikt Pschera, Marc Plitt, Frido Feldmeier, Shubham Bajpai, Christian Jestel, Nicolas Bach, Oliver Urbann, and S ¨oren Kerner. evobot – design and learning-based control of a two-wheeled compound inverted pendulum robot. In 2023 ...

2023
[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. 3

2020
[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. 3

2022 arXiv
[16]

Gdrnpp, 2022

Xingyu Liu, Ruida Zhang, Chenyangguang Zhang, Bowen Fu, Jiwen Tang, Xiquan Liang, Jingyi Tang, Xiaotian Cheng, Yukang Zhang, Gu Wang, and Xiangyang Ji. Gdrnpp, 2022. 3

2022
[17]

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. 3

2024
[18]

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. 3

2023
[19]

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, 2024. 3

2024
[20]

Maxime Oquab, Timoth ´ee Darcet, Theo Moutakanni, Huy V . V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Rus- sell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang- Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Ni...

2023
[21]

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, 2024. 3

2024
[22]

Sam 2: Segment anything in images and videos

Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman R¨adle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junt- ing Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao- Yuan Wu, Ross Girshick, Piotr Doll´ar, and Christoph Feic...

2024 arXiv
[23]

Latent-class hough forests for 3d object detection and pose estimation

Alykhan Tejani, Danhang Tang, Rigas Kouskouridas, and Tae-Kyun Kim. Latent-class hough forests for 3d object detection and pose estimation. In Computer Vision–ECCV 2014, pages 462–477. Springer, 2014. 2

2014
[24]

Tsai and R.K

R.Y . Tsai and R.K. Lenz. A new technique for fully au- tonomous and efficient 3d robotics hand/eye calibration. IEEE Transactions on Robotics and Automation , 5(3):345– 358, 1989. 4

1989
[25]

6-dof pose estimation of household objects for robotic manipulation: An accessible dataset and benchmark

Stephen Tyree, Jonathan Tremblay, Thang To, Jia Cheng, Terry Mosier, Jeffrey Smith, and Stan Birchfield. 6-dof pose estimation of household objects for robotic manipulation: An accessible dataset and benchmark. In International Con- ference on Intelligent Robots and Systems (I...

2022
[26]

Vggt: Visual geometry grounded transformer

Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 5

2025
[27]

BundleSDF: Neural 6-DoF tracking and 3D reconstruction of unknown objects

Bowen Wen, Jonathan Tremblay, Valts Blukis, Stephen Tyree, Thomas M ¨uller, Alex Evans, Dieter Fox, Jan Kautz, and Stan Birchfield. BundleSDF: Neural 6-DoF tracking and 3D reconstruction of unknown objects. In CVPR, 2023. 4

2023
[28]

FoundationPose: Unified 6d pose estimation and tracking of novel objects

Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchfield. FoundationPose: Unified 6d pose estimation and tracking of novel objects. In CVPR, 2024. 3, 6

2024
[29]

Simulation modeling of highly dynamic omnidirectional mobile robots based on real-world data

Marvin Wiedemann, Ossama Ahmed, Anna Dieckh ¨ofer, Re- nato Gasoto, and S ¨oren Kerner. Simulation modeling of highly dynamic omnidirectional mobile robots based on real-world data. In 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages 16923–16929,

2024
[30]

Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes

Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox. Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes. arXiv preprint arXiv:1711.00199, 2017. 1, 2

2017 arXiv

Pith tools

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

[1] [1]

Mtevent: A multi-task event camera dataset for 6d pose estimation and moving object detection

Shrutarv Awasthi, Anas Gouda, Sven Franke, J ´erˆome Ruti- nowski, Frank Hoffmann, and Moritz Roidl. Mtevent: A multi-task event camera dataset for 6d pose estimation and moving object detection. In Proceedings of the Computer Vi- sion and Pattern Recognition Conference, pages 5063–5071,

work page

[2] [2]

Intro- ducing hot3d: An egocentric dataset for 3d hand and object tracking

Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon, Shreyas Hampali, Fan Zhang, Jade Fountain, Edward Miller, Selen Basol, Richard Newcombe, Robert Wang, et al. Intro- ducing hot3d: An egocentric dataset for 3d hand and object tracking. arXiv preprint arXiv:2406.09598, 2024. 1

arXiv 2024

[3] [3]

Strobl, Matthias Humt, and Rudolph Triebel

Maximilian Denninger, Dominik Winkelbauer, Martin Sun- dermeyer, Wout Boerdijk, Markus Knauer, Klaus H. Strobl, Matthias Humt, and Rudolph Triebel. Blenderproc2: A procedural pipeline for photorealistic rendering. Journal of Open Source Software, 8(82):4901, 2023. 4

work page 2023

[4] [4]

Introducing mvtec itodd - a dataset for 3d object recognition in industry

Bertram Drost, Markus Ulrich, Paul Bergmann, Philipp Hartinger, and Carsten Steger. Introducing mvtec itodd - a dataset for 3d object recognition in industry. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2017. 1, 2

work page 2017

[5] [5]

Learning embeddings with centroid triplet loss for object identification in robotic grasp- ing

Anas Gouda, Max Schwarz, Christopher Reining, Sven Behnke, and Alice Kirchheim. Learning embeddings with centroid triplet loss for object identification in robotic grasp- ing. In 2024 IEEE 20th International Conference on Au- tomation Science and Engineering (CASE) , pages 3577– 3583, 2024. 3, 6

work page 2024

[6] [6]

Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions

Andrew Guo, Bowen Wen, Jianhe Yuan, Jonathan Tremblay, Stephen Tyree, Jeffrey Smith, and Stan Birchfield. Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 11428–11435, 2023. 1

work page 2023

[7] [7]

Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes

Stefan Hinterstoisser, Vincent Lepetit, Slobodan Ilic, Ste- fan Holzer, Gary Bradski, Kurt Konolige, and Nassir Navab. Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In Com- puter vision–ACCV 2012: 11th asian conference on com- puter vision, daejeon, korea, november 5-9, 2012, revised selected p...

work page 2012

[8] [8]

T-LESS: An RGB-D dataset for 6D pose estimation of texture-less ob- jects

Tom ´aˇs Hoda ˇn, Pavel Haluza, ˇStˇep´an Obdrˇz´alek, Jiˇr´ı Matas, Manolis Lourakis, and Xenophon Zabulis. T-LESS: An RGB-D dataset for 6D pose estimation of texture-less ob- jects. IEEE Winter Conference on Applications of Computer Vision (WACV), 2017. 1, 2

work page 2017