Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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

Pith's one-line read A two-stage CNN with texture-randomized training estimates a noncooperative spacecraft's pose from a single image to within about 3 degrees rotation and under 25 cm translation on synthetic validation data, and cuts the challenge score on…

desk verdict The architecture is solid and worth a cite; the texture-randomization result is plausible but the headline number is selected on the test set, so treat 0.513 as a favorable upper bound, not a settled gain. read the letter →

arxiv 1909.00392 v1 pith:TWROM6SV submitted 2019-09-01 cs.CV

Tae Ha Park , Sumant Sharma , Simone D'Amico This is my paper · ORCID
classification cs.CV
keywords spacecraftposeestimationnoncooperativerendezvousmonocularvisionconvolutionalneuralnetworkkeypointregressiontexturerandomizationstyletransfer
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 sets out to prove that a compact two-stage convolutional network can estimate the full 6-DOF relative pose of a known but noncooperative spacecraft from one monocular image, and that the synthetic-to-real gap can be narrowed by randomizing texture during training. The first stage finds the spacecraft and crops it; the second regresses the 2D locations of fixed surface keypoints, which feed a PnP solver to recover pose without feature matching. On synthetic validation data the pipeline reaches a mean rotation error of 3.097 degrees and translation errors below 25 cm. On 25 real spaceborne images unseen in training, mixing texture-randomized images into the training set at 50% probability improves the challenge score from 0.927 to 0.513 at the best epoch. The same experiments show that when textures are randomized, regressing the 3D bounding-box corners beats regressing surface keypoints.

What carries the argument

The machinery is the ordered-keypoint pose pipeline combined with style-based texture randomization. The KRN outputs a fixed vector of keypoint coordinates in a predefined order, so correspondence with 3D model points is known by construction and no matching or RANSAC is needed; EPnP then solves the pose. The texture-randomization mechanism is a neural style transfer pipeline with a random style embedding $z = \alpha \mathcal{N}(\mu, \Sigma) + (1-\alpha) P(c)$, with $\alpha = 0.25$, applied offline to synthetic images; it is meant to strip local surface texture and force the CNN to rely on global shape. Depthwise-separable convolutions throughout keep the network at 11.17 million total parameters and give the KRN roughly 30 FPS on a desktop CPU.

What would settle it

Split the 25 real spaceborne images into a validation half and a test half; use the validation half to choose the texture-randomization probability and the epoch, then compare pTR=0.5 against pTR=0 on the test half. If the held-out advantage of pTR=0.5 disappears or reverses, the claimed sim-to-real improvement from texture randomization is not established. A cheaper check is to repeat the training on a different spacecraft mock-up and see whether the same pTR=0.5 gain appears.

Watch

Extended reading notes

Core claim

The central claim is that coordinated design of detection, keypoint regression, and training data makes single-image spacecraft pose estimation both accurate and cheap enough for on-orbit use. The object detector (ODN), built on a MobileNet-style backbone, predicts a bounding box; the keypoint network (KRN) crops that region and regresses eleven ordered 2D keypoints; EPnP converts the 2D-3D correspondences into a pose estimate in one pass. The paper reports mean rotation error of 3.097 degrees and translation errors of [0.010, 0.011, 0.210] m on the synthetic validation set. For the secondary claim, the paper says that applying neural style transfer to randomize spacecraft texture at pTR=0.5 lowers the challenge score on unseen real spaceborne images from 0.927 (no style randomization) to 0.513 at the best epoch, and that KRN-BB (3D bounding-box corners) consistently outperforms KRN-SK (surface keypoints) under this augmentation because style transfer disrupts local geometry more than global shape.

Load-bearing premise

The result rests on the assumption that the single texture-randomization setting used here makes synthetic spacecraft look varied enough to cover the appearance of real orbital imagery, and that the measured improvement on the 25 real images is not just an artifact of choosing the training mix and stopping epoch by looking at those same images.

Editorial extensions

If this is right

  • A single monocular camera and a lightweight CNN suffice for real-time pose estimation of a known noncooperative spacecraft, since the combined network runs at about 70 FPS on a GPU and 4 FPS on a desktop CPU.
  • RoI cropping makes the pipeline robust to targets that are very close, very far, or clipped, because keypoint regression sees a zoomed-in region rather than the full 1920-by-1200 frame.
  • Texture randomization offers a way to improve sim-to-real transfer for spacecraft pose networks without collecting or labeling real spaceborne images.
  • When style-randomized training data are used, 3D bounding-box corners are a better regression target than surface keypoints because they track global shape rather than local surface detail.

Reading between the lines

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

  • If texture randomization works by forcing shape bias, the same training recipe should transfer to other known spacecraft, not just the Tango mock-up; a natural test is to run the KRN-BB variant on a different spacecraft with its own real images.
  • The choice of style strength and pTR is picked using the same 25 real images used for evaluation; a stricter evaluation would split those images into validation and test halves to avoid selection bias.
  • The finding that global-shape keypoints survive style transfer better than local-feature keypoints suggests a design heuristic: when training with strong augmentation, choose keypoints whose locations are defined by the object's outline rather than by surface detail.
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

3 major / 5 minor

Summary. This paper proposes a two-stage convolutional neural network for monocular 6-DOF pose estimation of a known noncooperative spacecraft. An object detection network (ODN) based on MobileNet and YOLOv3-style predictions localizes the spacecraft; a keypoint regression network (KRN) regresses 2D locations of either 11 surface keypoints or the centroid plus eight 3D bounding-box corners, and the pose is obtained via EPnP. On the SPEED synthetic validation set the architecture achieves mean translation error [0.010, 0.011, 0.210] m and mean rotation error 3.097 deg, and it placed 4th in the SLAB/ESA Pose Estimation Challenge. The second contribution is a texture-randomization training procedure that applies Neural Style Transfer to synthetic spacecraft images; the paper claims that training the KRN on a 50% mixture of style-randomized images improves the SLAB/ESA score on the 25-image PRISMA25 real dataset from 0.927 to 0.513 at the best epoch, and that bounding-box corner regression is more robust than surface keypoints under texture randomization.

Significance. The architecture contribution is externally validated by the 4th-place finish on the SLAB/ESA challenge and by public SPEED benchmarks; if reproduced, it provides a computationally efficient and reasonably accurate baseline for spacecraft pose estimation. The texture-randomization contribution addresses an important problem (sim-to-real transfer for orbital imagery) and the paper is transparent about training volatility. However, the central evidence for the texture-randomization claim is weakened because the mixture probability pTR and the reporting epoch are selected on the PRISMA25 test set itself, and because the best-epoch improvements do not persist at the last epoch. As a result, the significance of the second contribution, as presented, is limited to a proof of concept that requires a more rigorous evaluation protocol.

major comments (3)
  1. [Experiment 2: Texture Randomization, Table 6] The reported best-epoch scores are selected by evaluating the training curves on PRISMA25, which is the same 25-image set used to compare pTR values; this is a selection-on-test protocol and the claimed improvement (0.513 vs 0.927) is therefore not an unbiased estimate of generalization to new real images. The last-epoch scores in the same table do not show a robust advantage for pTR=0.5 (0.943±0.158) over pTR=0 (1.388±0.494) or pTR=0.25 (0.884±0.280). Please fix the model-selection rule (e.g., early stopping on a synthetic validation split or a pre-specified epoch count) and report the corresponding PRISMA25 scores, or clearly label the current numbers as optimistic and exploratory.
  2. [Experiment 2: Texture Randomization, Tables 6 and 7] The evaluation set contains only 25 images, and the reported three-seed standard deviations overlap substantially across pTR values (e.g., pTR=0.25: 0.717±0.276; pTR=0.5: 0.513±0.102; pTR=0.75: 0.849±0.133 in the best-epoch rows). Please report per-image score distributions, confidence intervals for the mean, and a paired statistical test for the pTR=0 vs pTR=0.5 comparison; without this, the claim that pTR=0.5 'consistently achieves the lowest' score is not supported.
  3. [Abstract and Section 'Texture Randomization'] The claim that texture randomization 'improves the network's performance on spaceborne images' is justified only by the PRISMA25 set, which is also used to select pTR and the reporting epoch; the style-strength parameter alpha=0.25 and the ImageNet-derived style distribution are additional free parameters with no independent validation that they span the real spacecraft texture domain. Please evaluate on a genuinely held-out real-image set with fixed hyperparameters, or add a sensitivity analysis over alpha and a domain-coverage check; otherwise the conclusion should be narrowed to a demonstration on PRISMA25 with acknowledged selection bias.
minor comments (5)
  1. [Section 'Experiments'] There is a typo 'Secion' in the paragraph introducing KRN-SK and KRN-BB; also the terms 'best epoch' and 'last epoch' in Tables 6 and 7 should be defined precisely (best according to what criterion?).
  2. [Experiment 2, Figure 10] Figure 10 appears to use a nonlinear y-axis scale but this is not stated; the claim that pTR=0.5 'outperforms the other study cases in general throughout the training' should be quantified, e.g., by the fraction of epochs with lowest score or by an area-under-curve comparison.
  3. [Object Detection Network] The statement that replacing convolutions with depth-wise separable convolutions 'reduces computation by a factor of 8 or 9' is more directly stated about parameter count; consider reporting FLOPs as well, since the factor of reduction in parameters is given by Eq. (4).
  4. [Single Image Pose Estimation, Eq. (1)] Eq. (1) uses the same symbol s_j for the scaling factor in every keypoint's projection; it would be clearer to define s_j as the homogeneous scale for the j-th image, and to state that the optimization is convex in the concatenated variables (s_j, p_{3D,k}).
  5. [Table 5 and accompanying text] The caption says 'the recovered keypoints are used as labels during training,' but the main experiment description says the ground-truth wireframe model is used; please clarify why the challenge models were trained with recovered keypoints and whether this affects comparability of the reported scores.

Circularity Check

1 steps flagged · score 6.0 of 10

Texture-randomization improvement is selected on the 25-image PRISMA25 test set (pTR and best epoch), so the sim-to-real gain is partly a selection artifact; the architecture claim remains externally benchmarked.

  1. fitted input called prediction [Experiment 2: Texture Randomization; Table 6 and Figure 10]
    "Table 6 reports the SLAB/ESA scores of the KRN-BB on PRISMA25 with varying pTR. Specifically, the experiments are run three times with different random seeds to check the consistency in training behavior, and the averaged scores are reported using the network after the best-performing epoch (Best) and the last epoch (Last)."

    PRISMA25 is the evaluation set for the sim-to-real claim, and it is also the set used to select pTR and to choose the reported epoch. Figure 10 plots SLAB/ESA scores on PRISMA25 during training, so the 'Best' row in Table 6 is the minimum of each training curve on the same 25 spaceborne images that were used to decide that pTR=0.50 is best. The claimed improvement (0.513 vs 0.927) is therefore an in-sample optimum, not a prediction on unseen data. The paper's own last-epoch numbers (0.943 +/- 0.158 for pTR=0.5 vs 1.388 +/- 0.494 for pTR=0) show no robust advantage, confirming that the headline depends on test-set model selection rather than on an independent evaluation.

full rationale

The architecture contribution is grounded externally: the 4th-place SLAB/ESA challenge result (Table 5) and the SPEED synthetic validation numbers (Table 3) use public/hosted benchmarks with fixed test splits, so those results are not circular. The self-citations to Sharma and D'Amico for SPN and SPEED are normal references to a public dataset and prior baseline, not load-bearing self-justification. The only significant circular step is the texture-randomization study: pTR and the reporting epoch are selected by inspecting PRISMA25 scores, and the same PRISMA25 scores are then reported as evidence of sim-to-real improvement. Because the last-epoch comparison does not show a consistent advantage, the central texture-randomization claim reduces, in part, to test-set selection. This warrants a partial circularity score of 6 rather than a higher score, since the architectural results stand independently and the authors disclose the volatility of the PRISMA25 scores.

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

The central claim rests on a publicly available 3D wireframe model of the Tango spacecraft, a single-object synthetic dataset (SPEED), and a pre-trained style transfer network's embedding statistics. The architecture experiment is grounded in an external challenge benchmark; the texture randomization experiment is the main source of circular fitting because pTR and the reporting epoch are selected on the PRISMA25 test set. No new physical entities are introduced.

free parameters (4)
  • pTR (texture-randomized training mixture probability) = 0.5
    Probability of sampling a texture-randomized image during training; selected from {0, 0.25, 0.5, 0.75} based on SLAB/ESA score on the PRISMA25 test set, the same set used to report improvement.
  • alpha (style strength in NST sampling) = 0.25
    Weight in z = alpha*N(mu, Sigma) + (1-alpha)*P(c) that mixes random style sampling with content embedding; chosen by hand without ablation.
  • Anchor boxes for ODN = 9 anchors from k-means clustering
    Bounding box priors pre-defined via k-means on training labels, as in YOLOv3; these enter the object detection loss.
  • Recovered 3D keypoint coordinates = 11 points with 5.7 mm mean error vs wireframe
    Used for PnP; recovered by optimizing reprojection error over 12 training images in Eq. (1), so pose accuracy is bounded by their accuracy.
assumptions (4)
  • domain assumption A known 3D model of the target spacecraft is available.
    The entire pose estimation pipeline relies on a pre-defined wireframe model of Tango from PRISMA; stated in the introduction and conclusion, where the authors note model-based methods assume known geometry.
  • domain assumption SPEED images contain exactly one known spacecraft.
    The ODN performs no classification and directly outputs objectness; stated in the Object Detection Network section: 'Since SPEED guarantees the presence of a single, known spacecraft in every image, no classification is performed.'
  • ad hoc to paper NST style embeddings from ImageNet provide a sufficient randomization distribution for spacecraft textures.
    The paper uses a pre-trained style embedding network and samples z = alpha*N(mu, Sigma) + (1-alpha)*P(c) with alpha=0.25; the adequacy of this distribution for spacecraft textures is not independently validated.
  • standard math The EPnP solver returns a correct pose given accurate 2D-3D correspondences.
    The final pose is computed with the existing EPnP algorithm; the paper relies on this standard solver.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft." pith.science (2026). https://pith.science/paper/TWROM6SV

@misc{pith2026190900392,
  author       = {Pith},
  title        = {Pith review of: Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TWROM6SV}},
  note         = {Machine review of arXiv:1909.00392}
}
read the original abstract

This work presents a novel Convolutional Neural Network (CNN) architecture and a training procedure to enable robust and accurate pose estimation of a noncooperative spacecraft. First, a new CNN architecture is introduced that has scored a fourth place in the recent Pose Estimation Challenge hosted by Stanford's Space Rendezvous Laboratory (SLAB) and the Advanced Concepts Team (ACT) of the European Space Agency (ESA). The proposed architecture first detects the object by regressing a 2D bounding box, then a separate network regresses the 2D locations of the known surface keypoints from an image of the target cropped around the detected Region-of-Interest (RoI). In a single-image pose estimation problem, the extracted 2D keypoints can be used in conjunction with corresponding 3D model coordinates to compute relative pose via the Perspective-n-Point (PnP) problem. These keypoint locations have known correspondences to those in the 3D model, since the CNN is trained to predict the corners in a pre-defined order, allowing for bypassing the computationally expensive feature matching processes. This work also introduces and explores the texture randomization to train a CNN for spaceborne applications. Specifically, Neural Style Transfer (NST) is applied to randomize the texture of the spacecraft in synthetically rendered images. It is shown that using the texture-randomized images of spacecraft for training improves the network's performance on spaceborne images without exposure to them during training. It is also shown that when using the texture-randomized spacecraft images during training, regressing 3D bounding box corners leads to better performance on spaceborne images than regressing surface keypoints, as NST inevitably distorts the spacecraft's geometric features to which the surface keypoints have closer relation.

Figures

Figures reproduced from arXiv: 1909.00392 by the authors.

Figure 1
Figure 1. Definition of the body reference frame ( [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Overall architecture of the proposed CNN. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. 11 keypoints used in the proposed architecture visualized on a wireframe [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Average of reprojection error of recovered 3D keypoints plotted against [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Different convolution operations. In this work, conventional convolution [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Examples of 6 images from PRISMA12K-TR inference, this NST pipeline allows for randomly sampling a vector of style embedding z ∈ R100 . Specifically, the style embedding is sampled as z = αN (µ, Σ) + (1 − α)P(c) (10) where P(c) is the style embedding of the content ima…
Figure 7
Figure 7. Figure 7: Examples of the predicted 2D bounding boxes and pose solutions of the [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Four worst pose solutions on the SPEED validation set. The 2D bounding [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Mean ||ET||2 and ER plotted against mean relative distance, ||tBC||2, for the SPEED validation set. The shaded region shows 25 and 75 percentile values. for all range of inter-spacecraft separation, and unlike SPN, the clipping due to proximity does not cause spike in …
Figure 10
Figure 10. Figure 10: SLAB/ESA scores on PRISMA25 during training. [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: Examples of bad texture randomization [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]

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. GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative Spacecraft

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A graph-convolution branch fused into a UNet heatmap detector reduces keypoint error for monocular spacecraft pose estimation, with the largest gains on symmetric and occluded targets in a new synthetic dataset.

Reference graph

Works this paper leans on

49 extracted references · 27 canonical work pages · cited by 1 Pith paper

  1. [1]

    RemoveDEBRIS: An in-orbit active debris removal demon- stration mission,

    J. L. Forshaw, G. S. Aglietti, N. Navarathinam, H. Kadhem, T. Salmon, A. Pisseloup, E. Joffre, T. Chabot, I. Retat, R. Axthelm, and e. al., “RemoveDEBRIS: An in-orbit active debris removal demon- stration mission,” Acta Astronautica, V ol. 127, 2016, p. 448463, 10.1016/j.actaastro.2016.06.018

    doi:10.1016/j.actaastro.2016.06.018 2016

  2. [2]

    DARPA Phoenix Payload Orbital Delivery System (PODs): FedEx to GEO,

    B. Sullivan, D. Barnhart, L. Hill, P. Oppenheimer, B. L. Benedict, G. V . Ommering, L. Chappell, J. Ratti, and P. Will, “DARPA Phoenix Payload Orbital Delivery System (PODs): FedEx to GEO,”AIAA SPACE 2013 Conference and Exposition, 2013, 10.2514/6.2013-5484

    doi:10.2514/6.2013-5484 2013

  3. [3]

    The Restore-L Servicing Mission,

    B. B. Reed, R. C. Smith, B. J. Naasz, J. F. Pellegrino, and C. E. Bacon, “The Restore-L Servicing Mission,” Aiaa Space 2016, 2016, 10.2514/6.2016-5478

    doi:10.2514/6.2016-5478 2016

  4. [4]

    Pose estimation of an uncooperative spacecraft from actual space imagery,

    S. D’Amico, M. Benn, and J. L. Jørgensen, “Pose estimation of an uncooperative spacecraft from actual space imagery,” International Journal of Space Science and Engineering , V ol. 2, No. 2, 2014, p. 171, 10.1504/ijspacese.2014.060600

    work page arXiv 2014

  5. [5]

    Robust Model-Based Monocular Pose Initialization for Nonco- operative Spacecraft Rendezvous,

    S. Sharma, J. Ventura, and S. DAmico, “Robust Model-Based Monocular Pose Initialization for Nonco- operative Spacecraft Rendezvous,”Journal of Spacecraft and Rockets, 2018, p. 116, 10.2514/1.a34124

    doi:10.2514/1.a34124 2018

  6. [6]

    Monocular-Based Pose Determination of Uncooperative Known and Unknown Space Objects,

    V . Capuano, K. Kim, J. Hu, A. Harvard, and S.-J. Chung, “Monocular-Based Pose Determination of Uncooperative Known and Unknown Space Objects,”69th International Astronautical Congress (IAC), 2018

    work page 2018

  7. [7]

    Comparative assessment of techniques for initial pose estimation using monocular vision,

    S. Sharma and S. D’Amico, “Comparative assessment of techniques for initial pose estimation using monocular vision,” Acta Astronautica, V ol. 123, 2016, p. 435445, 10.1016/j.actaastro.2015.12.032

    doi:10.1016/j.actaastro.2015.12.032 2016

  8. [8]

    Viewpoints and keypoints,

    S. Tulsiani and J. Malik, “Viewpoints and keypoints,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, 10.1109/cvpr.2015.7298758

    arXiv 2015

Show all 49 references
  1. [9]

    Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views,

    H. Su, C. R. Qi, Y . Li, and L. J. Guibas, “Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views,” 2015 IEEE International Conference on Computer Vision (ICCV), 2015, 10.1109/iccv.2015.308

    2015 doi

  2. [10]

    Fast Single Shot Detection and Pose Estimation,

    P. Poirson, P. Ammirato, C. Fu, W. Liu, J. Kosecka, and A. C. Berg, “Fast Single Shot Detection and Pose Estimation,” CoRR, V ol. abs/1609.05590, 2016. 18

    2016 arXiv

  3. [11]

    SSD-6D: Making RGB-Based 3D Detec- tion and 6D Pose Estimation Great Again,

    W. Kehl, F. Manhardt, F. Tombari, S. Ilic, and N. Navab, “SSD-6D: Making RGB-Based 3D Detec- tion and 6D Pose Estimation Great Again,” 2017 IEEE International Conference on Computer Vision (ICCV), 2017, 10.1109/iccv.2017.169

    2017 doi

  4. [12]

    Implicit 3D Orientation Learning for 6D Object Detection from RGB Images,

    M. Sundermeyer, Z.-C. Marton, M. Durner, M. Brucker, and R. Triebel, “Implicit 3D Orientation Learning for 6D Object Detection from RGB Images,” The European Conference on Computer Vision (ECCV), September 2018

    2018

  5. [13]

    PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization,

    A. Kendall, M. Grimes, and R. Cipolla, “PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization,” 2015 IEEE International Conference on Computer Vision (ICCV) , 2015, 10.1109/iccv.2015.336

    2015 doi

  6. [14]

    3D Pose Regression Using Convolutional Neural Networks,

    S. Mahendran, H. Ali, and R. Vidal, “3D Pose Regression Using Convolutional Neural Networks,” 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) , 2017, 10.1109/ic- cvw.2017.254

    2017 doi

  7. [15]

    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,” Robotics: Science and Systems XIV , 2018, 10.15607/rss.2018.xiv.019

    2018 doi

  8. [16]

    DeepIM: Deep Iterative Matching for 6D Pose Estima- tion,

    Y . Li, G. Wang, X. Ji, Y . Xiang, and D. Fox, “DeepIM: Deep Iterative Matching for 6D Pose Estima- tion,” Computer Vision ECCV 2018 Lecture Notes in Computer Science , 2018, p. 695711

    2018

  9. [17]

    BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth,

    M. Rad and V . Lepetit, “BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth,” 2017 IEEE International Conference on Computer Vision (ICCV), 2017, 10.1109/iccv.2017.413

    2017 doi

  10. [18]

    Real-Time Seamless Single Shot 6D Object Pose Prediction,

    B. Tekin, S. N. Sinha, and P. Fua, “Real-Time Seamless Single Shot 6D Object Pose Prediction,”CVPR, 2018

    2018

  11. [19]

    Deep Object Pose Estima- tion for Semantic Robotic Grasping of Household Objects,

    J. Tremblay, T. To, B. Sundaralingam, Y . Xiang, D. Fox, and S. Birchfield, “Deep Object Pose Estima- tion for Semantic Robotic Grasping of Household Objects,” CoRR abs/1809.10790, 2018

    2018 arXiv

  12. [20]

    Estimating 6D Pose From Localizing Desig- nated Surface Keypoints,

    Z. Zhao, G. Peng, H. Wang, H. Fang, C. Li, and C. Lu, “Estimating 6D Pose From Localizing Desig- nated Surface Keypoints,”ArXiv, V ol. abs/1812.01387, 2018

    2018 arXiv

  13. [21]

    PVNet: Pixel-wise V oting Network for 6DoF Pose Estimation,

    S. Peng, Y . Liu, Q. Huang, X. Zhou, and H. Bao, “PVNet: Pixel-wise V oting Network for 6DoF Pose Estimation,” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Oral , 2019

    2019

  14. [22]

    YOLO9000: Better, Faster, Stronger,

    J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,”2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 10.1109/cvpr.2017.690

    2017 doi

  15. [23]

    EPnP: An Accurate O(n) Solution to the PnP Problem,

    V . Lepetit, F. Moreno-Noguer, and P. Fua, “EPnP: An Accurate O(n) Solution to the PnP Problem,” International Journal of Computer Vision, V ol. 81, No. 2, 2008, p. 155166, 10.1007/s11263-008-0152- 6

    2008 doi

  16. [24]

    YOLOv3: An Incremental Improvement,

    J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” CoRR, V ol. abs/1804.02767, 2018

    2018 arXiv

  17. [25]

    Deep Residual Learning for Image Recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” arXiv preprint arXiv:1512.03385, 2015

    2015 arXiv

  18. [26]

    Distinctive Image Features from Scale-Invariant Keypoints,

    D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, V ol. 60, No. 2, 2004, p. 91110, 10.1023/b:visi.0000029664.99615.94

    2004

  19. [27]

    Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,

    M. A. Fischler and R. C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Readings in Computer Vision , 1987, p. 726740, 10.1016/b978-0-08-051581-6.50070-2

    1987 doi

  20. [28]

    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,” Computer Vision ACCV 2012 Lecture Notes in Computer Science , 2013, p. 548562

    2012

  21. [29]

    Learning 6D Object Pose Estimation Using 3D Object Coordinates,

    E. Brachmann, A. Krull, F. Michel, S. Gumhold, J. Shotton, and C. Rother, “Learning 6D Object Pose Estimation Using 3D Object Coordinates,” Computer Vision ECCV 2014 Lecture Notes in Computer Science, 2014, p. 536551

    2014

  22. [30]

    Pose estimation for non-cooperative spacecraft rendezvous using convolutional neural networks,

    S. Sharma, C. Beierle, and S. D’Amico, “Pose estimation for non-cooperative spacecraft rendezvous using convolutional neural networks,” 2018 IEEE Aerospace Conference , March 2018, pp. 1–12, 10.1109/AERO.2018.8396425

    2018

  23. [31]

    J.-F. Shi, S. Ulrich, and S. Ruel, CubeSat Simulation and Detection using Monocular Camera Images and Convolutional Neural Networks, 10.2514/6.2018-1604

    2018 doi

  24. [32]

    Pose Estimation for Non-Cooperative Rendezvous Using Neural Net- works,

    S. Sharma and S. D’Amico, “Pose Estimation for Non-Cooperative Rendezvous Using Neural Net- works,” 2019 AAS/AIAA Astrodynamics Specialist Conference, Ka’anapali, Maui, HI , January 13-17 2019

    2019

  25. [33]

    D’Amico, P

    S. D’Amico, P. Bodin, M. Delpech, and R. Noteborn, “PRISMA,”Distributed Space Missions for Earth System Monitoring Space Technology Library (M. D’Errico, ed.), V ol. 31, ch. 21, pp. 599–637, 2013, 10.1007/978-1-4614-4541-8 21. 19

    2013 doi

  26. [34]

    Random sample consensus: a paradigm for model fitting with ap- plications to image analysis and automated cartography,

    M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with ap- plications to image analysis and automated cartography,” Communications of the ACM , V ol. 24, No. 6, 1981, p. 381395, 10.1145/358669.358692

    1981

  27. [35]

    ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.,

    R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, “ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.,” Interna- tional Conference on Learning Representations , 2019

    2019

  28. [36]

    Style Augmentation: Data Augmentation via Style Randomization,

    P. T. Jackson, A. A. Abarghouei, S. Bonner, T. P. Breckon, and B. Obara, “Style Augmentation: Data Augmentation via Style Randomization,” 2018

    2018

  29. [37]

    CVX: Matlab Software for Disciplined Convex Programming, version 2.1,

    M. Grant and S. Boyd, “CVX: Matlab Software for Disciplined Convex Programming, version 2.1,” http://cvxr.com/cvx, Mar. 2014

    2014

  30. [38]

    Graph implementations for nonsmooth convex programs,

    M. Grant and S. Boyd, “Graph implementations for nonsmooth convex programs,” Recent Advances in Learning and Control (V . Blondel, S. Boyd, and H. Kimura, eds.), Lecture Notes in Control and Infor- mation Sciences, pp. 95–110, Springer-Verlag Limited, 2008. http://stanford.edu...

    2008

  31. [39]

    MobileNetV2: Inverted Residuals and Linear Bottlenecks,

    M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , June 2018, pp. 4510–4520, 10.1109/CVPR.2018.00474

    2018

  32. [40]

    MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,

    A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” 2017. cite arxiv:1704.04861

    2017 arXiv

  33. [41]

    You Only Look Once: Unified, Real-Time Ob- ject Detection,

    J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Ob- ject Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, 10.1109/cvpr.2016.91

    2016 doi

  34. [42]

    Generalized Intersection over Union,

    H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, “Generalized Intersection over Union,” June 2019

    2019

  35. [43]

    ImageNet Classification with Deep Convolutional Neu- ral Networks,

    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neu- ral Networks,” Advances in Neural Information Processing Systems (NIPS) , 2012, pp. 11061114,

    2012

  36. [44]

    Microsoft COCO: Common Objects in Context,

    T.-Y . Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, P. Perona, D. Ramanan, P. Doll´ar, and C. L. Zitnick, “Microsoft COCO: Common Objects in Context,”ECCV, 2014

    2014

  37. [45]

    Very Deep Convolutional Networks for Large-Scale Image Recogni- tion,

    K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recogni- tion,” CoRR, 2014

    2014

  38. [46]

    Arbitrary Style Transfer in Real-time with Adaptive Instance Normaliza- tion,

    X. Huang and S. Belongie, “Arbitrary Style Transfer in Real-time with Adaptive Instance Normaliza- tion,” ICCV, 2017

    2017

  39. [47]

    Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude,

    T. Tieleman and G. Hinton, “Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude,” COURSERA: Neural Networks for Machine Learning, 2012

    2012

  40. [48]

    Decoupled Weight Decay Regularization,

    I. Loshchilov and F. Hutter, “Decoupled Weight Decay Regularization,” International Conference on Learning Representations, 2019

    2019

  41. [49]

    Random Erasing Data Augmentation,

    Z. Zhong, L. Zheng, G. Kang, S. Li, and Y . Yang, “Random Erasing Data Augmentation,”arXiv preprint arXiv:1708.04896, 2017. 20

    2017 arXiv

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.