Pith. sign in

REVIEW 4 major objections 8 minor 1 cited by

Vision6D: 3D-to-2D Interactive Visualization and Annotation Tool for 6D Pose Estimation

T0 review · 4 major / 8 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Vision6D claims that by interactively dragging a 3D model over a 2D photograph, a user can produce 6D pose annotations statistically comparable to dataset ground truth—mean angular errors of 4.77 degrees on Linemod and 5.88 degrees on…

desk verdict Vision6D is a real, open-source annotation tool with a plausible interface, but the evaluation's best-of-three and top-90% selections inflate the headline accuracy numbers, leaving the statistical-comparability claim unsupported. read the letter →

arxiv 2504.15329 v1 pith:EQZHK7VJ submitted 2025-04-21 cs.GR cs.CVcs.HCcs.RO

Yike Zhang , Eduardo Davalos , Jack Noble This is my paper · ORCID
classification cs.GRcs.CVcs.HCcs.RO
keywords 6Dposeestimationannotation3D-to-2DregistrationinteractivevisualizationuserstudypinholecameramodelLinemodHANDAL
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

Vision6D is an open-source desktop tool that lets a person drag a 3D object model on top of a 2D photograph until its projected outline matches the object in the image, and then exports the implied 6D camera pose. The paper argues this is the first purpose-built interactive 3D-to-2D annotation system for 6D pose estimation, and backs it with a user study on the Linemod and HANDAL datasets. In the study, each user's best of three annotations landed within mean angular errors of 4.77 degrees (Linemod) and 5.88 degrees (HANDAL) of dataset ground truth, with mean annotation times around 100 seconds. If true, this gives pose-estimation researchers a way to generate training labels for custom or prerecorded scenes using only the camera intrinsic matrix, without fiducial markers or motion capture.

What carries the argument

The load-bearing object is the pinhole projection model of Equation (3), $P_{uvw} = K M P_w$, where $K$ is the camera intrinsic matrix and $M=[R|t]$ is the unknown extrinsic pose the user is annotating. Given $K$, aligning the rendered 3D model to the 2D image determines $M$ by construction. The supporting machinery is the multi-view interface: a free-navigation scene camera for spatial understanding, an original-camera view locked to the photograph, and immediate re-projection on drag, so the user closes the loop between 3D manipulation and 2D evidence.

What would settle it

Take a set of images from Linemod or HANDAL, deliberately perturb the supplied $K$ (for example, scale focal lengths by 5 to 10 percent while leaving the principal point fixed), have users annotate with Vision6D, and measure ADD against ground truth. If mean error does not grow substantially, the claim survives; if it grows in proportion to the perturbation, the claim's dependence on exact intrinsics is confirmed.

Watch

Extended reading notes

Core claim

The central discovery is that a 3D-to-2D interactive alignment task, mediated by the pinhole projection equation $P_{uvw} = K[R|t]P_w$, is accurate and efficient enough for humans to reproduce dataset-grade 6D poses. Users manipulate meshes in a 3D scene while a synchronized original-camera view re-renders the projection in real time; the final matrix $[R|t]$ is read out directly from the aligned model. Across 11 participants, annotations were statistically comparable to ground truth, with inter-personal mean ADD of 15.27 mm on Linemod and 30.53 mm on HANDAL, intra-personal repeatability around 7 to 10 degrees of angular distance, and NASA-TLX and SUS feedback indicating moderate workload and generally good usability. The paper frames this as filling the gap between existing 3D GUIs and the need for fast, marker-free 6D pose labels.

Load-bearing premise

The entire pipeline assumes the camera intrinsic matrix $K$ is known and correct for every image; any error in focal length or principal point shifts the projection in Equation (3) and biases every annotation, no matter how perfect the user's visual alignment.

Editorial extensions

If this is right

  • Pose labels can be produced for arbitrary images and custom objects without fiducial markers or known camera extrinsics, requiring only the intrinsic matrix $K$.
  • First-frame annotations can seed video sequences or downstream tracking and pose-estimation training pipelines.
  • Texture-less and cluttered objects, such as those in Linemod-Occluded scenes, can be annotated by direct 3D-to-2D overlay rather than by feature matching.
  • Mean annotation times around 100 seconds per sample make small-scale dataset labeling practical for research groups without specialized capture equipment.
  • The reported accuracy and repeatability metrics support using Vision6D annotations as pseudo-ground-truth when dataset ground truth is unavailable.

Reading between the lines

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

  • If the camera intrinsics are uncertain, the tool's practical ceiling may be set by calibration quality rather than user skill; a calibration-refinement step or in-tool estimation of $K$ would be the natural next test.
  • The same interaction loop could be extended to semi-automatic pose propagation, where the user annotates only the first frame and tracked 2D features propose subsequent poses for correction.
  • Combining Vision6D with a learned initial pose estimate from a rough detector or keypoint-based PnP could cut the roughly 100-second annotation time substantially, turning the tool into a refinement interface.
  • The best-of-three protocol suggests that majority voting or an automated consistency check across repeated annotations could further improve final pose quality.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 8 minor

Summary. Vision6D is an interactive 3D-to-2D visualization and annotation tool for 6D object pose estimation. The paper describes the tool's interface and mathematical formalism (projection through a pinhole camera model using a known intrinsic matrix K), then presents a user study with 11 participants who annotated 10 samples from the Linemod and HANDAL datasets three times each. The study measures inter-personal and intra-personal annotation errors against ground-truth poses using angular distance, Euclidean distance, and ADD, plus annotation time and NASA-TLX/SUS questionnaires. The central claim is that Vision6D enables users to produce 6D pose annotations 'statistically comparable' to ground truth, with reported best-of-three mean angular errors of 4.77° (Linemod) and 5.88° (HANDAL).

Significance. If the accuracy and efficiency results are confirmed after correcting the evaluation protocol, Vision6D would be a valuable open-source contribution to the 6D pose annotation community. The paper uses external ground truth from standard datasets, applies commonly accepted metrics, and includes usability instruments (NASA-TLX, SUS). The reproducible open-source release is a clear strength. However, the current evaluation contains selection bias and lacks inferential statistics, so the headline accuracy numbers and the 'statistically comparable' claim (H1) are not yet established. With appropriate revisions, the tool's contribution could be solid.

major comments (4)
  1. [IV.B.4, V.A.1] The evaluation protocol selects the best of each participant's three trials by lowest ADD and additionally reports only the 'top 90%' of results to eliminate outliers. Both procedures condition on the error being measured: the best-of-three selection cannot be replicated by an end user who does not know the ground truth, and the top-90% trimming removes the worst trials without a principled criterion. Consequently, the reported mean angular errors (4.77° for Linemod, 5.88° for HANDAL) are best-case post-hoc bounds, not expected annotation accuracy. The paper's own intra-personal data support this concern: the mean intra-personal angular distance is 7.11° (Linemod) and 10.08° (HANDAL), larger than the corresponding inter-personal best-of-three means, showing that trial-to-trial variability is large enough to change the headline metric. Please report results for all trials, for the first trial only, and the number of trials excluded by the trimming rule.
  2. [IV.A (H1), V.A] Hypothesis H1 claims that Vision6D annotations are 'statistically comparable' to ground truth, but the results section provides only descriptive statistics and no statistical test, confidence interval, or equivalence bound. After the selection procedures described above, the descriptive statistics cannot support an inferential claim. To substantiate H1, the authors should pre-specify an equivalence margin for the angular and ADD metrics and apply a standard equivalence test (e.g., two one-sided tests) using the full per-trial data, reporting confidence intervals and effect sizes.
  3. [III.A, Eq. (3), IV.B.4] The projection pipeline depends entirely on the camera intrinsic matrix K being known and correct. The paper acknowledges that the tool uses 'only the camera intrinsic matrix,' but it performs no sensitivity analysis for errors in K, and it does not discuss how estimated intrinsics (e.g., from a calibration algorithm or a different camera) would affect annotation accuracy. This is a key limitation for real-world use, since users of the tool may have imperfect intrinsics. Please add a sensitivity experiment that perturbs the components of K (fx, fy, cx, cy) over a plausible range and reports the resulting increase in angular and ADD errors, or explicitly state the accuracy requirement on K.
  4. [VI, V.A.1] The study does not address object symmetries in the evaluation. The limitation section correctly notes that symmetric and textureless objects create pose ambiguities, but the ADD metric used in Equations (4)-(5) is not symmetry-aware. If any of the selected Linemod or HANDAL objects have rotational symmetries, a visually correct annotation could be scored as a large ADD error, potentially biasing the reported accuracy. The authors should either exclude symmetric objects from the accuracy analysis, use a symmetry-aware metric (e.g., ADD-S), or justify that the selected objects do not have symmetries.
minor comments (8)
  1. [IV.B.4] The phrase 'we present the top 90% results to eliminate outliers' is ambiguous; specify the unit of trimming (participants, trials, or samples) and the rationale for the 90% threshold.
  2. [Table I] The table rows are labeled L0/L1... and H0/H1..., but the text refers to participants as L0 to L4 and H0 to H5; the connection to samples S0-S9 is unclear. The table should include the overall mean and standard deviation reported in the text.
  3. [III.B] The example uses the Linemod-Occluded dataset [21], while the user study uses Linemod [10]; clarify the relationship between the datasets to avoid confusion.
  4. [Throughout] Typographical and notation issues: 'LineMod' and 'Linemod' are used inconsistently; the acronym 'ADD' is written as 'Add' in the captions of Figures 4 and 5; and Equation (3) uses 'Puvw' without explicitly defining the subscript convention. Please ensure consistent terminology.
  5. [Figures 4, 5, 7] Figures 4, 5, and 7 are referenced but do not appear in the manuscript text; in the final version, ensure that all figures are embedded with clear axis labels, legends, and statistical annotations.
  6. [VII (Conclusion)] The conclusion states that Vision6D 'has supported several deep-learning-based studies' [24]-[28]; please briefly describe the role of the tool in these studies to support this claim.
  7. [IV.B (Stimuli)] The limitation section discusses symmetric objects, but the user study section does not report which specific objects were selected from Linemod and HANDAL, making it impossible to assess the impact of symmetries and object difficulty on the reported errors. Please include the object list and, if relevant, object-level error analysis.
  8. [IV.B (Study Design)] The study does not include a baseline annotation method (e.g., manual numerical pose entry or another interactive tool). While not required to validate the tool's internal accuracy, a comparison would strengthen the claim that Vision6D is more efficient and intuitive than alternatives.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central user-study evaluation is empirical and benchmarked against external ground-truth poses.

full rationale

Vision6D's claimed capability is evaluated by comparing human-made annotations to ground-truth poses from Linemod and HANDAL, which are external datasets. The projection formalism (Eq. 3) is the standard pinhole camera model and is not fit to the evaluation data; no parameter is estimated from annotations and then renamed as a prediction. The reported angular/ADD errors are measured outcomes, not quantities derived by construction from the tool's equations. The only self-referential passages are the conclusion's statement that Vision6D 'has supported several deep-learning-based studies, including those proposed in [24]-[28]' and references to the authors' prior work; these are usage claims and are not load-bearing assumptions for the accuracy or efficiency results. The top-90% trimming and best-of-three selection (Sec. IV.B.4 and V.A.1) are evaluation-protocol concerns that may overstate accuracy and undermine the 'statistically comparable' phrasing, but they are not circularity: the headline errors are not forced to equal an input by construction. No self-definitional, fitted-input, uniqueness-imported, or ansatz-via-citation step was found.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim rests on standard camera projection, the accuracy of external ground-truth poses, and the user's ability to visually resolve pose from a rendered overlay. No free parameters are fitted to data, and no new entities are postulated.

assumptions (3)
  • standard math Pinhole camera model accurately describes image formation for the input images.
    Used in Section III-A to derive the projection equation (3) that underlies the annotation alignment. Standard computer vision model, not specific to this paper.
  • domain assumption Ground-truth poses in Linemod and HANDAL datasets are accurate references.
    The user study compares annotations against these ground truths (Section IV-B.2). If the ground truths are noisy, reported errors are not purely annotation error.
  • domain assumption Users can visually disambiguate the correct 6D pose from the rendered 3D-to-2D overlay.
    The tool relies on human visual judgment for alignment (Section III-B.2), which the paper itself identifies as challenging for symmetric or textureless objects (Section VI).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Vision6D: 3D-to-2D Interactive Visualization and Annotation Tool for 6D Pose Estimation." pith.science (2026). https://pith.science/paper/EQZHK7VJ

@misc{pith2026250415329,
  author       = {Pith},
  title        = {Pith review of: Vision6D: 3D-to-2D Interactive Visualization and Annotation Tool for 6D Pose Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EQZHK7VJ}},
  note         = {Machine review of arXiv:2504.15329}
}
read the original abstract

Accurate 6D pose estimation has gained more attention over the years for robotics-assisted tasks that require precise interaction with physical objects. This paper presents an interactive 3D-to-2D visualization and annotation tool to support the 6D pose estimation research community. To the best of our knowledge, the proposed work is the first tool that allows users to visualize and manipulate 3D objects interactively on a 2D real-world scene, along with a comprehensive user study. This system supports robust 6D camera pose annotation by providing both visual cues and spatial relationships to determine object position and orientation in various environments. The annotation feature in Vision6D is particularly helpful in scenarios where the transformation matrix between the camera and world objects is unknown, as it enables accurate annotation of these objects' poses using only the camera intrinsic matrix. This capability serves as a foundational step in developing and training advanced pose estimation models across various domains. We evaluate Vision6D's effectiveness by utilizing widely-used open-source pose estimation datasets Linemod and HANDAL through comparisons between the default ground-truth camera poses with manual annotations. A user study was performed to show that Vision6D generates accurate pose annotations via visual cues in an intuitive 3D user interface. This approach aims to bridge the gap between 2D scene projections and 3D scenes, offering an effective way for researchers and developers to solve 6D pose annotation related problems. The software is open-source and publicly available at https://github.com/InteractiveGL/vision6D.

Figures

Figures reproduced from arXiv: 2504.15329 by the authors.

Figure 1
Figure 1. 3D-to-2D Projection. Demonstration of projecting 3D object point onto a 2D image plane using the camera intrinsic and extrinsic parameters. After the 3D scene is transformed into the camera’s co￾ordinate system using the transformations above, the next key step is projection and rendering. These are the processes for mapping 3D points [X,Y,Z] from the camera coordinate system onto the 2D image plane. A pinhole camer… view at source ↗
Figure 2
Figure 2. Vision6D’s 3D User Interface. Screenshot and decomposition of essential 2D and 3D features to support the interactive 6D pose annotation workflow. fined as Puvw = KPc where Puvw is the homogeneous 2D pixel coordinate of the projected point on the uv image plane. Puvw =     fx 0 cx 0 0 fy cy 0 0 0 1 0 0 0 0 1         r1 r2 r3 t1 r4 r5 r6 t2 r7 r8 r9 t3 0 0 0 1         X Y Z 1     (3) This proj… view at source ↗
Figure 3
Figure 3. Visualization and Pose Annotation of Vision6D [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Quantitative Evaluation of Inter-Personal Variability. This figure presents the distributions of Angular Distance, Euclidean Distance, and ADD metrics for assessing inter-personal variability. The results demonstrate the effectiveness of pose annotation using Vision6D.…
Figure 5
Figure 5. Figure 5: Quantitative Evaluation of Intra-Personal Consistency. This figure presents the distributions of Angular Distance, Euclidean Distance, and ADD metrics for assessing intra-personal consistency. The results highlight the robustness and reproducibility of the Vision6D. an…
Figure 6
Figure 6. Figure 6: Qualitative Evaluation of Inter-personal Variability. This evaluation compares user annotations for 10 objects across two datasets, showing (1) the original image, (2) user-annotated pose, (3) ground-truth pose, and (4) pose differences. V. RESULTS We evaluated the acc…
Figure 7
Figure 7. Figure 7: User Feedback. We demonstrate the user feedback using two standardized evaluation metrics: NASA-TLX for assessing cognitive workload and task difficulty, and SUS for measuring system usability. the Linemod dataset required an average of 102.19 ± 31.93 seconds per annot…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation

    cs.LG 2026-03 conditional novelty 6.0 of 10

    2DV sample selection beats random and farthest-first traversal for biomedical time-series annotation when labels are aggregated, but shows higher label-distribution risk under tight single-annotator budgets.

Reference graph

Works this paper leans on

30 extracted references · 20 canonical work pages · cited by 1 Pith paper

  1. [1]

    A survey of 6dof object pose estimation methods for different application scenarios,

    J. Guan, Y . Hao, Q. Wu, S. Li, and Y . Fang, “A survey of 6dof object pose estimation methods for different application scenarios,” Sensors, vol. 24, no. 4, 2024. [Online]. Available: https://www.mdpi.com/1424-8220/24/4/1076

    2024

  2. [2]

    Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation

    H. Wang, S. Sridhar, J. Huang, J. Valentin, S. Song, and L. J. Guibas, “Normalized object coordinate space for category-level 6d object pose and size estimation,” 2019. [Online]. Available: https://arxiv.org/abs/1901.02970

    work page Pith review arXiv 2019

  3. [3]

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

    Y . Xiang, T. Schmidt, V . Narayanan, and D. Fox, “Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes,” 2018. [Online]. Available: https://arxiv.org/abs/1711.00199 JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 12

    arXiv 2018

  4. [4]

    The moped framework: Object recognition and pose estimation for manipulation,

    A. Collet, M. Martinez, and S. Srinivasa, “The moped framework: Object recognition and pose estimation for manipulation,” I. J. Robotic Res. , vol. 30, pp. 1284–1306, 09 2011

    work page 2011

  5. [5]

    Deepim: Deep iterative matching for 6d pose estimation,

    Y . Li, G. Wang, X. Ji, Y . Xiang, and D. Fox, “Deepim: Deep iterative matching for 6d pose estimation,” International Journal of Computer Vision, vol. 128, no. 3, p. 657–678, Nov. 2019. [Online]. Available: http://dx.doi.org/10.1007/s11263-019-01250-9

    doi:10.1007/s11263-019-01250-9 2019

  6. [6]

    A review of 6d object pose esti- mation,

    Y . Zhu, M. Li, W. Yao, and C. Chen, “A review of 6d object pose esti- mation,” in 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), vol. 10, 2022, pp. 1647– 1655

    work page 2022

  7. [7]

    Densefusion: 6d object pose estimation by iterative dense fusion,

    C. Wang, D. Xu, Y . Zhu, R. Mart ´ın-Mart´ın, C. Lu, L. Fei-Fei, and S. Savarese, “Densefusion: 6d object pose estimation by iterative dense fusion,” 2019. [Online]. Available: https://arxiv.org/abs/1901.04780

    arXiv 2019

  8. [8]

    Pvnet: Pixel-wise voting network for 6dof pose estimation,

    S. Peng, Y . Liu, Q. Huang, H. Bao, and X. Zhou, “Pvnet: Pixel-wise voting network for 6dof pose estimation,” 2018. [Online]. Available: https://arxiv.org/abs/1812.11788

    arXiv 2018

Show all 30 references
  1. [9]

    Peng, Image Formation

    X. Peng, Image Formation. Boston, MA: Springer US, 2009, pp. 722–

    2009

  2. [10]

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

    S. Hinterstoisser, V . Lepetit, S. Ilic, S. Holzer, G. Bradski, K. Konolige, and N. Navab, “Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes,” in Computer Vision – ACCV 2012 , K. M. Lee, Y . Matsushita, J. M. Rehg, and ...

    2012

  3. [11]

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

    A. Guo, B. Wen, J. Yuan, J. Tremblay, S. Tyree, J. Smith, and S. Birchfield, “Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions,”

  4. [12]

    Blender (software) — Wikipedia, the free encyclopedia,

    W. contributors, “Blender (software) — Wikipedia, the free encyclopedia,” 2024, [Online; accessed 7-September-2024]. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Blender (software)&oldid=1242772657

    2024

  5. [13]

    Blender for robotics: Integration into the leuven paradigm for robot task specification and human motion estimation,

    K. Buys, T. De Laet, R. Smits, and H. Bruyninckx, “Blender for robotics: Integration into the leuven paradigm for robot task specification and human motion estimation,” 11 2010, pp. 15–25

    2010

  6. [14]

    Unity (game engine) — Wikipedia, the free encyclo- pedia,

    W. contributors, “Unity (game engine) — Wikipedia, the free encyclo- pedia,” https://en.wikipedia.org/w/index.php?title=Unity (game engine) &oldid=1242730215, 2024, [Online; accessed 7-September-2024]

    2024

  7. [15]

    3d modelling and visualization based on the unity game engine – advantages and challenges,

    I. Buyuksalih, S. Bayburt, G. Buyuksalih, A. Baskaraca, H. Karim, and A. Rahman, “3d modelling and visualization based on the unity game engine – advantages and challenges,” vol. IV-4/W4, 11 2017, pp. 161– 166

    2017

  8. [16]

    Pangolin: Lightweight 3d visualization and interface library,

    S. Lovegrove, “Pangolin: Lightweight 3d visualization and interface library,” https://github.com/stevenlovegrove/Pangolin

  9. [17]

    Real-time monocular slam: Accurate localization and mapping using point map and search-by-projection approach,

    A. Khole, A. Thakar, S. Shende, and V . Karajkhede, “Real-time monocular slam: Accurate localization and mapping using point map and search-by-projection approach,” May 2023. [Online]. Available: http://dx.doi.org/10.36227/techrxiv.22819913.v1

    2023 doi

  10. [18]

    T-less: An rgb-d dataset for 6d pose estimation of texture- less objects,

    T. Hodan, P. Haluza, S. Obdrzalek, J. Matas, M. Lourakis, and X. Zabulis, “T-less: An rgb-d dataset for 6d pose estimation of texture- less objects,” 2017. [Online]. Available: https://arxiv.org/abs/1701.05498

    2017 arXiv

  11. [19]

    Intro- ducing mvtec itodd — a dataset for 3d object recognition in industry,

    B. Drost, M. Ulrich, P. Bergmann, P. H ¨artinger, and C. Steger, “Intro- ducing mvtec itodd — a dataset for 3d object recognition in industry,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 2200–2208

    2017

  12. [20]

    Automatic generation and detection of highly reliable fiducial markers under occlusion,

    S. Garrido-Jurado, R. Mu ˜noz-Salinas, F. Madrid-Cuevas, and M. Mar´ın- Jim´enez, “Automatic generation and detection of highly reliable fiducial markers under occlusion,” Pattern Recognition , vol. 47, no. 6, pp. 2280–2292, 2014. [Online]. Available: https://www.sciencedirect...

    2014

  13. [21]

    Learning 6d object pose estimation using 3d object coordi- nates,

    E. Brachmann, A. Krull, F. Michel, S. Gumhold, J. Shotton, and C. Rother, “Learning 6d object pose estimation using 3d object coordi- nates,” in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014,...

    2014

  14. [22]

    Development of nasa-tlx (task load index): Results of empirical and theoretical research,

    S. G. Hart and L. E. Staveland, “Development of nasa-tlx (task load index): Results of empirical and theoretical research,” in Human Mental Workload, ser. Advances in Psychology, P. A. Hancock and N. Meshkati, Eds. North-Holland, 1988, vol. 52, pp. 139–183. [Online]. Available...

    1988

  15. [23]

    Sus: A quick and dirty usability scale,

    J. Brooke, “Sus: A quick and dirty usability scale,” Usability Eval. Ind., vol. 189, 11 1995

    1995

  16. [24]

    A first step in using machine learning methods to enhance interaction analysis for embodied learning environments,

    J. Fonteles, E. Davalos, T. S. Ashwin, Y . Zhang, M. Zhou, E. Ayalon, A. Lane, S. Steinberg, G. Anton, J. Danish, N. Enyedy, and G. Biswas, “A first step in using machine learning methods to enhance interaction analysis for embodied learning environments,” in Artificial Intell...

    2024

  17. [25]

    Monocular microscope to CT registration using pose estimation of the incus for augmented reality cochlear implant surgery,

    Y . Zhang, E. Davalos, D. Su, A. Lou, and J. H. Noble, “Monocular microscope to CT registration using pose estimation of the incus for augmented reality cochlear implant surgery,” in Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling , J. H. Sie...

    2024 doi

  18. [26]

    Ssdd-gan: Single-step denoising diffusion gan for cochlear implant surgical scene completion,

    Y . Zhang, E. Davalos, and J. Noble, “Ssdd-gan: Single-step denoising diffusion gan for cochlear implant surgical scene completion,” 2025. [Online]. Available: https://arxiv.org/abs/2502.05710

    2025 arXiv

  19. [27]

    Post-mastoidectomy surface multi-view synthesis from a single microscopy image,

    Y . Zhang and J. Noble, “Post-mastoidectomy surface multi-view synthesis from a single microscopy image,” 2025. [Online]. Available: https://arxiv.org/abs/2409.03190

    2025 arXiv

  20. [28]

    3d gaze tracking for studying collaborative interactions in mixed-reality environments,

    E. Davalos, Y . Zhang, A. T. S, J. H. Fonteles, U. Timalsina, and G. Biswas, “3d gaze tracking for studying collaborative interactions in mixed-reality environments,” in Companion Proceedings of the 26th International Conference on Multimodal Interaction , ser. ICMI Companion ...

    2024

  21. [725]

    Available: https://doi.org/10.1007/978-0-387-73003-5 654

    [Online]. Available: https://doi.org/10.1007/978-0-387-73003-5 654

    doi

  22. [2023]

    Available: https://arxiv.org/abs/2308.01477

    [Online]. Available: https://arxiv.org/abs/2308.01477

    arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.