PPS-Ctrl: Controllable Sim-to-Real Translation for Colonoscopy Depth Estimation

Andrea Dunn Beltran; Jun Myeong Choi; Marc Niethammer; Roni Sengupta; Xinqi Xiong

REVIEW 4 major objections 4 minor 30 references

PPS-Ctrl: Controllable Sim-to-Real Translation for Colonoscopy Depth Estimation

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

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Pith's one-line read The paper claims that conditioning a diffusion-based translator on a per-pixel shading map rather than a raw depth map preserves source geometry while adding realistic textures, and that this improves downstream colonoscopy depth…

desk verdict PPS-Ctrl shows a plausible and internally consistent win for shading-conditioned sim-to-real in colonoscopy, but the evaluation needs to grow up before the claims are taken at face value. read the letter →

arxiv 2504.17067 v1 pith:V6VLOVKF submitted 2025-04-23 cs.CV

Xinqi Xiong , Andrea Dunn Beltran , Jun Myeong Choi , Marc Niethammer , Roni Sengupta This is my paper · ORCID

classification cs.CV

keywords image-to-imagetranslationdepthestimationcolonoscopydiffusionmodelsControlNetper-pixelshadingsim-to-realtransferstructuralconditioning

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 is trying to establish that, when translating synthetic colonoscopy images into realistic ones, the structural condition passed to a diffusion model should be a per-pixel shading map rather than a depth map. Depth alone, the authors argue, lets the generator alter surface colors inconsistently in near-field and far-field regions, while shading encodes how light falls on each surface point and keeps the translation aligned with the source geometry. They build a Stable Diffusion pipeline with a ControlNet that takes an encoded PPS map as its conditioning input, and they show that depth estimators trained on their translated images beat those trained on a GAN-based baseline. If true, this offers a practical way to train depth estimators for real colonoscopy without collecting ground-truth clinical depth.

What carries the argument

The central object is the Per-Pixel Shading (PPS) map, defined from a depth map as $\mathrm{PPS}(x) = L_a(x)\,(L_d(x)^{\mathsf T} N(x))$, with $L_d(x) = (x-p_c)/\|x-p_c\|$ and $L_a(x) = \|x-p_c\|^{-2}$ for an assumed point light at $p_c$. The map is computed from the synthetic depth map $D(u,v)$, camera intrinsics $K$, and the derived surface normal $N(x)$, so it encodes how strongly each surface point would be lit by a near-field source. The machinery feeds this map through a learnable control encoder $E_C$ into a ControlNet attached to a fine-tuned latent diffusion model, and adds a control decoder $D_C$ that reconstructs the PPS map from the latent feature map; the reconstruction loss in Eq. (3) is summed with the diffusion denoising objective in Eq. (4). The load-bearing idea is that shading, not raw depth, is what the generator must respect to keep near-field and far-field geometry consistent while swapping textures across domains.

What would settle it

Measure how well the computed PPS map predicts actual image intensity (after albedo normalization) in near-field frames of a real colonoscopy video; a large mismatch would mean the conditioning signal is partly fictional. A second decisive experiment: train PPS-Ctrl with the light position shifted to the scope tip's true offset from the optical center and compare downstream RMSE on the phantom target; no improvement would suggest the specific lighting model is not what delivers the gain.

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Extended reading notes

Core claim

The central claim is that a Per-Pixel Shading (PPS) map, computed from a depth map as inverse-square light falloff times the dot product of surface normal and light direction, is a stronger structural condition for sim-to-real translation than the depth map itself. The authors build a latent diffusion pipeline in which a ControlNet takes an encoded PPS map as its conditioning input, with a control decoder that reconstructs the PPS from the latent features to keep the conditioning signal intact. On SimCol3D-to-C3VD translation, training the downstream depth estimator DepthAnything on the translated data yields RMSE 3.740 with the full PPS-plus-decoder configuration, versus 4.662 for MI-CycleGAN and 4.753 for no translation; the paper reports this as a 20% improvement over MI-CycleGAN. On C3VD-to-Colon10K translation to clinical video, FID improves from 0.498 for MI-CycleGAN to 0.297, and every ablation row shows PPS conditioning outperforming depth conditioning, with the control decoder helping further.

Load-bearing premise

The load-bearing premise is that colonoscopy lighting behaves like a single point light at a known position near the camera; if the real scope's light geometry differs enough, the shading map will encode wrong structure and the downstream gains could shrink.

Editorial extensions

If this is right

Depth estimators can be trained on translated synthetic images paired with the original synthetic depth maps, so the method does not need clinical ground-truth depth to improve target-domain depth prediction.
Using the PPS map instead of the depth map as the ControlNet condition improves downstream depth metrics in every ablation setting, and adding the control decoder improves them further.
The best configuration lowers RMSE on the phantom target from 4.662 (MI-CycleGAN) to 3.740 and lowers FID on clinical translation from 0.498 to 0.297.
The paper's qualitative results show fewer checkerboard artifacts and fewer false dark down-the-barrel textures than the GAN baseline, which matters for clinical usability.

Reading between the lines

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

One extension the paper leaves implicit is that PPS conditioning should transfer to other near-field endoscopic settings, such as laparoscopy or arthroscopy, wherever a usable depth map and camera-light geometry exist.
A decisive ablation the paper does not run is to randomize the assumed light position $p_c$ during training; if downstream depth accuracy does not degrade, the benefit may come from the encoder-decoder regularizer rather than the physical shading model.
The control decoder's PPS reconstruction could be turned into a self-supervised signal on real clinical video, allowing the shading model to be refined without paired depth.

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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

Colleague,

The useful thing about this paper is that it tests a concrete idea: instead of feeding a depth map to ControlNet for structure-preserving sim-to-real translation, feed a per-pixel shading (PPS) map computed from that depth under a near-field lighting model. The ablations in Table 1 back the idea up—PPS beats depth, and adding the control decoder helps further, with the best config getting RMSE 3.740 vs. 4.662 for MI-CycleGAN on C3VD. That is a meaningful, if contained, improvement for downstream depth estimation.

What's genuinely new is the combination. PPS maps come from near-field photometric stereo work, and ControlNet is established, but the specific use of PPS as a structural condition in a diffusion translation pipeline for endoscopy is not in the cited literature. The encoder-decoder that reconstructs PPS from the encoded feature is a small but sensible addition, and the paper correctly acknowledges that specularity and fine details remain uncontrolled.

The soft spots are matters of evidence, not conception. First, the FID values are implausibly low: 0.545, 0.437, 0.297. FIDs on real image distributions are usually in the tens, even for near-identical domains. Either they computed something else or the dataset is so small that the metric is meaningless. The paper doesn't explain this. Second, there are no error bars or multiple seeds in Table 1, and the only baseline is MI-CycleGAN. A 20% RMSE improvement is easy to over-read without a sense of variance. Third, the PPS computation in Eq. 2 depends on the light source position pc, which is never specified. In real colonoscopes the light is at the tip, offset from the optical center and not a point source. If pc is arbitrarily set to the camera center, the conditioning signal is miscalibrated exactly in the near-field regions that motivate the method. A sensitivity analysis of pc and at least a qualitative comparison with an alternative placement would make the claim reproducible. The stress-test note gets this right.

None of this sinks the paper. The depth estimation results are internally consistent and the ablation logic is sound. The paper deserves a serious referee, but it needs revisions: clarify the FID computation, add uncertainty estimates, expand the baseline set, and address the sensitivity of pc.

For a reading group on medical-image synthesis, I'd bring it. I wouldn't cite it in my own work yet.

Referee Report

4 major / 4 minor

Summary. The paper proposes PPS-Ctrl, a sim-to-real image translation framework for colonoscopy that uses Stable Diffusion with a ControlNet conditioned on a Per-Pixel Shading (PPS) map rather than on a depth map. The PPS map is computed from a depth map under a near-field point-light model, and an encoder-decoder is introduced to extract latent control features. The authors evaluate downstream depth estimation by training DepthAnything on translated SimCol3D-to-C3VD data, report FID scores for translation quality, and provide qualitative results on C3VD-to-Colon10K translation. The central claim is that PPS conditioning better preserves structure than depth conditioning, yielding a 20% RMSE improvement over MI-CycleGAN. Code is publicly available.

Significance. If the claims hold, the idea of using a physically motivated per-pixel shading field as the structural control signal in a diffusion-based translation pipeline is a useful contribution to endoscopic domain adaptation, where structure preservation is critical. The paper tests the central claim with a downstream depth-estimation task on out-of-domain phantom data, which avoids circularity, and it includes an ablation separating the PPS condition from the control decoder. The code release is a strength for reproducibility. However, the empirical support is currently incomplete: the light-source parameter in the PPS model is unspecified, the FID numbers are implausibly low and lack methodological details, and the downstream results are reported without variance or significance testing against more than one baseline.

major comments (4)

[Sec. 3.2, Eq. (2)] The PPS map is the sole structural control signal, but the paper never specifies how the light-source position pc is set. Eq. (2) defines PPS(x) = La(x)(Ld(x)^T N(x)) with Ld and La depending on pc; for a colonoscope, the light source is at the tip, offset from the optical center and spatially extended, so an arbitrary choice of pc changes the conditioning exactly in the near-field regions the method is designed to improve. Since the Table 1 advantage of Ours-PPS over Ours-Depth could depend on a favorable pc choice, the manuscript should state the value used for each dataset, justify it from the scope geometry, and include a sensitivity analysis over pc or a calibration procedure. Without this, the central claim is not reproducible as written.
[Table 2] The reported FID values (0.437 for SimCol3D-to-C3VD and 0.297 for C3VD-to-Colon10K) are orders of magnitude lower than typical FID values for full image distributions and are also lower than the corresponding no-translation FID values (0.545 and 0.527), which is suspicious because the untranslated source and target are different domains. The paper does not specify the feature layer, the number of samples, or the implementation used for FID; a mismatch in implementation would invalidate the realism claim. Please recompute FID with a standard implementation and report the sample size and confidence intervals.
[Table 1] The central quantitative claim rests on a single table without error bars, number of runs, or significance testing. The 20% RMSE improvement over MI-CycleGAN (4.662 vs. 3.740) and the 9% improvement of Ours-PPS w.DC over Ours-Depth w.DC (4.099 vs. 3.740) are plausible but unverified. Because the downstream depth metric is the main evidence for the PPS representation, please report mean and standard deviation over multiple training runs, perform a significance test, and add at least one additional strong baseline beyond MI-CycleGAN, such as a diffusion-only translation method without explicit structure conditioning, to support the comparison.
[Sec. 3.2 and Sec. 3.3] There is a train/inference text-prompt mismatch that is not discussed. Stage 2 trains the ControlNet with text prompts corresponding to the Sim domain (psource), while inference uses the Real-domain prompt (ptarget). Since the ControlNet is conditioned on text embeddings, this domain shift could affect conditioning behavior. The paper should either train with both prompts at appropriate sampling probabilities or justify why the prompt change does not interact with the PPS control signal.

minor comments (4)

[Eq. (2)] The notation for the inverse-square falloff is ambiguous: 'La(x) = 1 / ||x−pc||2^2' should be written as La(x) = ||x - pc||_2^{-2}, and the unit direction Ld should be defined explicitly as (x - pc)/||x - pc||_2.
[Keywords and Abstract] There are several typos: 'Image-to-imagetranslation', 'Depthestimation', 'Colonscopy' in the keywords, 'Freceht Inception Distance' in Sec. 4.1, and 'Ous-PPS withDC' in Sec. 4.4. These should be corrected.
[Sec. 3.3] The Control Encoder-Decoder architecture is described only as '4 residual blocks' and 'a transposed architecture of the control encoder EC'; please provide the encoder architecture, feature resolutions, and whether the decoder is applied at multiple scales, since this module is central to the ablation.
[Fig. 2] The caption reports absolute relative errors for PPS maps but does not explain how those errors are computed or which regions they aggregate over; please add a precise description in the caption or text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is tested by downstream depth accuracy on an external target dataset, not reduced to its inputs by construction.

full rationale

The paper's core claim is that conditioning a diffusion model on a Per-Pixel Shading (PPS) map preserves structure better than conditioning on a depth map. The PPS map is defined deterministically from depth by Eq. 2, and the same depth map is later used as supervision when training DepthAnything on the translated images. This is not circular: the evaluation measures whether the translated images retain enough geometric structure to train a depth estimator that generalizes to a held-out target domain (C3VD or Colon10K). The comparison between PPS and depth conditioning is an empirical ablation in Table 1, with no parameter fitted to the evaluation metric and then renamed as a prediction. The Control Encoder-Decoder reconstruction loss in Eq. 3 is a training auxiliary, not used as evidence for the main result. The self-citations to [15] and [3] provide background motivation for PPS in endoscopy but are not load-bearing: the paper's own controlled experiments and externally evaluated depth metrics support the claim independently. The unspecified light-source position pc in Eq. 2 is a reproducibility and robustness concern, not a circularity, because it does not make the downstream depth evaluation equivalent to the conditioning input by construction. Overall, the derivation chain is self-contained and empirically grounded.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central method introduces no new physical entities. The key assumptions are the endoscope lighting model (point source, no angular attenuation, source position pc) and the text-based domain separation. The learnable encoder-decoder is a model component, not an invented entity.

free parameters (1)

Light source position pc (used in PPS computation)
Eq. 2 requires a light-source location pc to compute PPS; the paper does not state whether pc is the camera center or estimated per dataset. If set to the camera center by hand, it is a chosen parameter that affects the conditioning signal.

assumptions (3)

domain assumption Near-field point light at pc with inverse-square falloff and no angular attenuation models endoscope illumination (Eq. 2)
Used to compute PPS from depth. Real colonoscopes have off-axis extended sources, so this is an approximation.
domain assumption Text prompts 'synthetic colonoscopy' and 'real colonoscopy' sufficiently separate the two domains in Stable Diffusion (Section 3.1)
The whole pipeline depends on the text-conditioned domain separation working as intended; the paper observes naive fine-tuning blends domains and provides no quantitative verification of separation.
domain assumption Depth maps paired with source images are always consistent with the rendered image such that PPS computed from depth aligns with image content
The training pairs in SimCol3D/C3VD are assumed to be registered; if misalignment exists, the conditioning signal is corrupted.

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Cite this review

Pith. "Pith review of PPS-Ctrl: Controllable Sim-to-Real Translation for Colonoscopy Depth Estimation." pith.science (2026). https://pith.science/paper/V6VLOVKF

@misc{pith2026250417067,
  author       = {Pith},
  title        = {Pith review of: PPS-Ctrl: Controllable Sim-to-Real Translation for Colonoscopy Depth Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V6VLOVKF}},
  note         = {Machine review of arXiv:2504.17067}
}

read the original abstract

Accurate depth estimation enhances endoscopy navigation and diagnostics, but obtaining ground-truth depth in clinical settings is challenging. Synthetic datasets are often used for training, yet the domain gap limits generalization to real data. We propose a novel image-to-image translation framework that preserves structure while generating realistic textures from clinical data. Our key innovation integrates Stable Diffusion with ControlNet, conditioned on a latent representation extracted from a Per-Pixel Shading (PPS) map. PPS captures surface lighting effects, providing a stronger structural constraint than depth maps. Experiments show our approach produces more realistic translations and improves depth estimation over GAN-based MI-CycleGAN. Our code is publicly accessible at https://github.com/anaxqx/PPS-Ctrl.

Figures

Figures reproduced from arXiv: 2504.17067 by the authors.

**Figure 1.** Our method takes a depth map from any synthetic colon datasets as input and generates textures similar to real endoscopy videos while preserving the depth in the generated image. Our proposed pipeline consists of a Stable Diffusion model that can capture image statistics of both real and synthetic domains using text prompts (trained in Stage 1), and a ControlNet that guides the diffusion model to perform depth-prese… view at source ↗

**Figure 2.** We calculate error between ground-truth per-pixel shading map and intensity of the original image (Col 4), image generated by depth conditioning (Col 6) and PPS condition (Col 8) to show that PPS conditioning significantly decreases structural inconsistency and closely matches that of the original image. 3.2 PPS-Ctrl for Depth Preservation Existing techniques for content preserving image generation [6,26], often use… view at source ↗

**Figure 3.** Comparison of depth estimation on the Colon10K dataset using DepthAnything [25], trained on C3VD→Colon10K translations from our method (DˆOurs) and MI-CycleGAN [22] (DˆMI−cGAN ), as well as models trained on C3VD alone without translation (DˆBase). Our translated data significantly enhances depth estimation compared to MI-CycleGAN and no translation, particularly in the regions highlighted by the black box. accura… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Comparison of C3VD → Colon10K translation results between our method and MI-CycleGAN [22], with similar views from Colon10K provided for reference. MI-CycleGAN often fails to preserve depth, introducing incorrect dark textures that mimic down-the-barrel views (Columns …

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

30 extracted references · 18 canonical work pages

[1]

Araghi, M., Soerjomataram, I., Jenkins, M., Brierley, J., Morris, E., Bray, F., Arnold, M.: Global trends in colorectal cancer mortality: projections to the year

work page
[2]

Scientific Data10(1), 671 (2023)

Azagra, P., Sostres, C., Ferrández, Á., Riazuelo, L., Tomasini, C., Barbed, O.L., Morlana, J., Recasens, D., Batlle, V.M., Gómez-Rodríguez, J.J., et al.: Endomap- per dataset of complete calibrated endoscopy procedures. Scientific Data10(1), 671 (2023)

2023
[3]

arXiv preprint arXiv:2412.13176 (2024)

Beltran, A.D., Rho, D., Niethammer, M., Sengupta, R.: Nfl-ba: Improv- ing endoscopic slam with near-field light bundle adjustment. arXiv preprint arXiv:2412.13176 (2024)

arXiv 2024
[4]

Medical image analysis 90, 102956 (2023)

Bobrow, T.L., Golhar, M., Vijayan, R., Akshintala, V.S., Garcia, J.R., Durr, N.J.: Colonoscopy 3d video dataset with paired depth from 2d-3d registration. Medical image analysis 90, 102956 (2023)

work page 2023
[5]

Cheng, K., Ma, Y., Sun, B., Li, Y., Chen, X.: Depth estimation for colonoscopy images with self-supervised learning from videos. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Confer- ence, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24. pp. 119–128. Springer (2021)

work page 2021
[6]

In: European Conference on Computer Vision

Cheng, T.Y., Sharma, P., Markham, A., Trigoni, N., Jampani, V.: Zest: Zero- shot material transfer from a single image. In: European Conference on Computer Vision. pp. 370–386. Springer (2024)

work page 2024
[7]

ScribbleLight: Single Image Indoor Relighting with Scribbles

Choi, J.M., Wang, A., Peers, P., Bhattad, A., Sengupta, R.: Scribblelight: Single image indoor relighting with scribbles. arXiv preprint arXiv:2411.17696 (2024)

work page Pith review arXiv 2024
[8]

In: International conference on medical image computing and computer-assisted intervention

Du, Y., Jiang, Y., Tan, S., Wu, X., Dou, Q., Li, Z., Li, G., Wan, X.: Arsdm: colonoscopy images synthesis with adaptive refinement semantic diffusion models. In: International conference on medical image computing and computer-assisted intervention. pp. 339–349. Springer (2023)

work page 2023

Show all 30 references

[9]

Communications of the ACM 63(11), 139–144 (2020) 10 X

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Communications of the ACM 63(11), 139–144 (2020) 10 X. Xiong et al

2020
[10]

arXiv preprint arXiv:2112.13595 (2021)

Jeong, B.H., Kim, H.K., Son, Y.D.: Depth estimation of endoscopy using sim-to- real transfer. arXiv preprint arXiv:2112.13595 (2021)

2021 arXiv
[11]

Kaleta, J., Dall’Alba, D., Płotka, S., Korzeniowski, P.: Minimal data requirement forrealisticendoscopicimagegenerationwithstablediffusion.Internationaljournal of computer assisted radiology and surgery19(3), 531–539 (2024)

2024
[12]

Scientific Reports 14(1), 872 (2024)

Kim, B.S., Cho, M., Chung, G.E., Lee, J., Kang, H.Y., Yoon, D., Cho, W.S., Lee, J.C., Bae, J.H., Kong, H.J., et al.: Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning. Scientific Reports 14(1), 872 (2024)

2024
[13]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Lichy, D., Sengupta, S., Jacobs, D.W.: Fast light-weight near-field photometric stereo. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12612–12621 (2022)

2022
[14]

In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

Ma, R., McGill, S.K., Wang, R., Rosenman, J., Frahm, J.M., Zhang, Y., Pizer, S.: Colon10k: a benchmark for place recognition in colonoscopy. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). pp. 1279–1283. IEEE (2021)

2021
[15]

In: European Conference on Computer Vision

Paruchuri, A., Ehrenstein, S., Wang, S., Fried, I., Pizer, S.M., Niethammer, M., Sengupta, R.: Leveraging near-field lighting for monocular depth estimation from endoscopy videos. In: European Conference on Computer Vision. pp. 473–491. Springer (2024)

2024
[16]

Medical Image Analysis96, 103195 (2024)

Rau, A., Bano, S., Jin, Y., Azagra, P., Morlana, J., Kader, R., Sanderson, E., Matuszewski, B.J., Lee, J.Y., Lee, D.J., et al.: Simcol3d—3d reconstruction during colonoscopy challenge. Medical Image Analysis96, 103195 (2024)

2024
[17]

Official journal of the American College of Gastroenterology| ACG 110(1), 72–90 (2015)

Rex, D.K., Schoenfeld, P.S., Cohen, J., Pike, I.M., Adler, D.G., Fennerty, B.M., Lieb, J.G., Park, W.G., Rizk, M.K., Sawhney, M.S., et al.: Quality indicators for colonoscopy. Official journal of the American College of Gastroenterology| ACG 110(1), 72–90 (2015)

2015
[18]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Rodríguez-Puigvert, J., Batlle, V.M., Montiel, J., Martinez-Cantin, R., Fua, P., Tardós, J.D., Civera, J.: Lightdepth: Single-view depth self-supervision from illu- mination decline. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 21273–21283 (2023)

2023
[19]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10684–10695 (2022)

2022
[20]

arXiv preprint arXiv:2408.09822 (2024)

Venkatesh, D.K., Rivoir, D., Pfeiffer, M., Speidel, S.: Surgical-cd: Generating surgi- cal images via unpaired image translation with latent consistency diffusion models. arXiv preprint arXiv:2408.09822 (2024)

2024 arXiv
[21]

Diagnostics11(12), 2264 (2021)

Wan, J., Chen, B., Yu, Y.: Polyp detection from colorectum images by using at- tentive yolov5. Diagnostics11(12), 2264 (2021)

2021
[22]

In: International Conference on Medical Image Computing and Computer-Assisted Intervention

Wang, S., Paruchuri, A., Zhang, Z., McGill, S., Sengupta, R.: Structure-preserving image translation for depth estimation in colonoscopy. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 667–677. Springer (2024)

2024
[23]

arXiv preprint arXiv:2407.16508 (2024)

Wu, Z., Jin, Y., Qiu, L., Han, X., Wan, X., Li, G.: Toder: Towards colonoscopy depth estimation and reconstruction with geometry constraint adaptation. arXiv preprint arXiv:2407.16508 (2024)

2024 arXiv
[24]

arXiv preprint arXiv:2411.12198 (2024) Title Suppressed Due to Excessive Length 11

Xie, Y., Wang, J., Feng, T., Ma, F., Li, Y.: Ccis-diff: A generative model with stable diffusion prior for controlled colonoscopy image synthesis. arXiv preprint arXiv:2411.12198 (2024) Title Suppressed Due to Excessive Length 11

2024 arXiv
[25]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., Zhao, H.: Depth anything: Un- leashing the power of large-scale unlabeled data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10371–10381 (2024)

2024
[26]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Zhang, F., You, S., Li, Y., Fu, Y.: Atlantis: Enabling underwater depth estimation with stable diffusion. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11852–11861 (2024)

2024
[27]

In: Proceedings of the IEEE/CVF international conference on computer vision

Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 3836–3847 (2023)

2023
[28]

IEEE Transactions on Medical Robotics and Bionics3(1), 85–95 (2020)

Zhang, S., Zhao, L., Huang, S., Ye, M., Hao, Q.: A template-based 3d recon- struction of colon structures and textures from stereo colonoscopic images. IEEE Transactions on Medical Robotics and Bionics3(1), 85–95 (2020)

2020
[29]

In: Proceedings of the IEEE interna- tional conference on computer vision

Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE interna- tional conference on computer vision. pp. 2223–2232 (2017)

2017
[2035]

International journal of cancer144(12), 2992–3000 (2019)

2019

Pith tools

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

[1] [1]

Araghi, M., Soerjomataram, I., Jenkins, M., Brierley, J., Morris, E., Bray, F., Arnold, M.: Global trends in colorectal cancer mortality: projections to the year

work page

[2] [2]

Scientific Data10(1), 671 (2023)

Azagra, P., Sostres, C., Ferrández, Á., Riazuelo, L., Tomasini, C., Barbed, O.L., Morlana, J., Recasens, D., Batlle, V.M., Gómez-Rodríguez, J.J., et al.: Endomap- per dataset of complete calibrated endoscopy procedures. Scientific Data10(1), 671 (2023)

2023

[3] [3]

arXiv preprint arXiv:2412.13176 (2024)

Beltran, A.D., Rho, D., Niethammer, M., Sengupta, R.: Nfl-ba: Improv- ing endoscopic slam with near-field light bundle adjustment. arXiv preprint arXiv:2412.13176 (2024)

arXiv 2024

[4] [4]

Medical image analysis 90, 102956 (2023)

Bobrow, T.L., Golhar, M., Vijayan, R., Akshintala, V.S., Garcia, J.R., Durr, N.J.: Colonoscopy 3d video dataset with paired depth from 2d-3d registration. Medical image analysis 90, 102956 (2023)

work page 2023

[5] [5]

Cheng, K., Ma, Y., Sun, B., Li, Y., Chen, X.: Depth estimation for colonoscopy images with self-supervised learning from videos. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Confer- ence, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24. pp. 119–128. Springer (2021)

work page 2021

[6] [6]

In: European Conference on Computer Vision

Cheng, T.Y., Sharma, P., Markham, A., Trigoni, N., Jampani, V.: Zest: Zero- shot material transfer from a single image. In: European Conference on Computer Vision. pp. 370–386. Springer (2024)

work page 2024

[7] [7]

ScribbleLight: Single Image Indoor Relighting with Scribbles

Choi, J.M., Wang, A., Peers, P., Bhattad, A., Sengupta, R.: Scribblelight: Single image indoor relighting with scribbles. arXiv preprint arXiv:2411.17696 (2024)

work page Pith review arXiv 2024

[8] [8]

In: International conference on medical image computing and computer-assisted intervention

Du, Y., Jiang, Y., Tan, S., Wu, X., Dou, Q., Li, Z., Li, G., Wan, X.: Arsdm: colonoscopy images synthesis with adaptive refinement semantic diffusion models. In: International conference on medical image computing and computer-assisted intervention. pp. 339–349. Springer (2023)

work page 2023