Pith. sign in

REVIEW 5 major objections 5 minor 20 references

Toward Patient-specific Partial Point Cloud to Surface Completion for Pre- to Intra-operative Registration in Image-guided Liver Interventions

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read Completing a partially visible liver surface with a rotation-equivariant occupancy network reduces rigid-registration error from roughly 33–40 mm to 3–5 mm in phantom tests.

desk verdict Patient-specific VN-OccNet completion plus Go-ICP cuts TRE from ~30-40 mm to ~3-5 mm on two phantoms, but the test partial clouds come from the same synthetic pipeline used in training, so clinical transfer is unproven. read the letter →

arxiv 2505.19518 v2 pith:WTKNWI2J submitted 2025-05-26 cs.CV

Nakul Poudel , Zixin Yang , Kelly Merrell , Richard Simon , Cristian A. Linte This is my paper · ORCID
classification cs.CV
keywords image-guidedliversurgerypointcloudcompletionoccupancynetworksSO(3)-equivariantpre-tointra-operativeregistrationtargeterrorpatient-specifictrainingphantomvalidation
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

During image-guided liver surgery, the intra-operative surface is only partially visible, which makes aligning it with the pre-operative model unreliable. This paper argues that the missing surface can be synthesized by a patient-specific occupancy network, VN-OccNet, trained on simulated deformations of the patient's own pre-operative liver model, and that registration performed on the completed surface is far more accurate than registration on the raw partial cloud. In phantom experiments, the completed surfaces produced target registration errors of 5.19 ± 1.34 mm and 3.35 ± 0.61 mm, compared to 33.13 ± 13.89 mm and 39.87 ± 15.07 mm for the original 30%-visibility point clouds. The paper also shows that VN-OccNet's rotation equivariance keeps completion quality consistent across arbitrary test rotations, where a standard OccNet degrades sharply.

What carries the argument

VN-OccNet is a vector-neuron occupancy network: the encoder represents point-cloud features as vectors in SO(3)-equivariant layers, and the decoder predicts occupancy probabilities for query points, with the architecture guaranteeing that a rotation of the input point cloud merely rotates the latent features rather than changing the reconstruction. The decoder outputs occupancy for a query grid; the Multiresolution Isosurface Extraction (MISE) algorithm adaptively subdivides surface-intersecting voxels down to a 128³ resolution, and Marching Cubes turns those voxels into a watertight mesh. A threshold c = 0.4 converts occupancy probabilities into inside/outside labels. The completed mesh's vertices are sampled as a dense target point cloud, and Go-ICP performs globally optimal rigid registration against the pre-operative source cloud. The full chain—completion in about 0.25 s plus registration in about 16 s—is what the experiments evaluate.

What would settle it

Take a real intra-operative liver surface where the true deformation is known from postoperative imaging or tracked fiducials, feed the partial cloud to the patient-specific network, and compare the completed surface to the true deformed surface; if the Chamfer distance or per-point error on real data is much larger than the in silico test values, or if Go-ICP on the completed surface gives TRE comparable to the raw partial cloud, the central claim would be contradicted.

Watch

Extended reading notes

Core claim

The central claim is that surface completion, rather than a better registration objective, is the effective remedy for partial intra-operative visibility. Using VN-OccNet—a rotation-equivariant occupancy network—the authors generate a complete watertight liver mesh from a partial point cloud, extract its vertices, and feed them to the Go-ICP rigid registration algorithm. On two in vitro phantoms, this reduces target registration error from roughly 33–40 mm at 30% visibility to 3–5 mm, a statistically significant improvement that persists even when the raw partial cloud is given more visibility (40% and 50%). The same network maintains its reconstruction accuracy across I/I, Z/Z, Z/SO(3), and SO(3)/SO(3) train/test configurations, whereas the non-equivariant OccNet baseline fails in the cross-rotation settings. The paper presents the result as a patient-specific pipeline: each patient's pre-operative model is deformed in simulation to create training data, so the network learns that patient's geometry and deformation patterns rather than a generic liver shape.

Load-bearing premise

The load-bearing premise is that the simulated deformations used for training—a soft-elastic finite-element model of the patient's liver, with stiffness 2–5 kPa and applied forces up to 3 N—match the way a real liver deforms during surgery; if real deformations fall outside this simulated range, the completed surfaces will be wrong and the registration gains will not transfer to patients.

Editorial extensions

If this is right

  • At 30% intra-operative visibility, registration with a completed surface achieves TRE of 5.19 ± 1.34 mm (phantom No. 1) and 3.35 ± 0.61 mm (phantom No. 3), several times smaller than the 33–40 mm TRE obtained with the raw partial cloud.
  • Increasing the raw cloud's visibility from 30% to 50% only reduces TRE to about 26 mm, still far worse than the completed-surface result, so completion buys more accuracy than acquiring more surface.
  • Because VN-OccNet's accuracy is stable across Z-axis and SO(3) rotations without rotation data augmentation, the method removes the need to pre-align intra-operative point clouds before completion.
  • The watertight mesh output enables uniform surface sampling, which the authors state is important for registration methods that assume consistent point density.
  • The patient-specific training strategy, using simulated neo-Hookean deformations of the pre-operative model, is what allows the network to generalize to the in vitro phantom deformations without generic liver training data.

Reading between the lines

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

  • If the completion step transfers to real intra-operative acquisition, initial rigid registration could become fully automatic: no manual correspondences, no fiducial initialization, and no pre-alignment, because the reconstruction is rotation-agnostic.
  • The same recipe—simulate deformations of the patient's own pre-operative anatomy, train an occupancy network, complete, then register—could be applied to other partially visible organs or to laparoscopic video surfaces, though the FEM simulation fidelity would be the main risk.
  • The paper itself acknowledges (Discussion) that real intra-operative clouds contain noise, holes, and occlusions absent from the training simulation, and that training a fresh network per patient may limit practicality; these caveats define the main risk to the central claim.
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

5 major / 5 minor

Summary. The paper proposes a patient-specific pipeline for rigid registration of pre-operative liver surfaces to partial intra-operative point clouds. A VN-OccNet occupancy network is trained on simulated deformations of a patient-specific liver model to complete partial point clouds into full watertight surfaces, and the completed surface is registered to the pre-operative source with Go-ICP. The authors evaluate the rotation equivariance of VN-OccNet against OccNet under four train/test rotation settings, and report Target Registration Error (TRE) on two in vitro phantoms, showing large improvements when using completed surfaces (5.19 ± 1.34 mm and 3.35 ± 0.61 mm for phantoms No. 1 and No. 3) over the original partial clouds at 30% visibility (33.13 ± 13.89 mm and 39.87 ± 15.07 mm).

Significance. If the reported TRE gains hold under realistic intra-operative conditions, the approach would offer a useful step toward addressing partial visibility in image-guided liver interventions without manual initialization or rigid pre-registration. The systematic comparison of VN-OccNet and OccNet under different rotation settings is a genuine empirical contribution, and the patient-specific simulation strategy is well motivated. However, the empirical support is narrow: only two in vitro phantoms are used, the training and test partial-cloud generation pipelines are matched, and no comparison is made with prior completion-based registration methods. These limitations do not invalidate the proof-of-concept, but they substantially constrain the strength of the clinical claim as currently stated.

major comments (5)
  1. [§3.4, Table 2] The TRE values in Table 2 and the Wilcoxon Rank Sum test in Section 4.2 are computed over 500 rotated target clouds per phantom, but these 500 clouds are generated by applying 100 random rotations to only five viewpoint-specific partial clouds. The repeated rotations are not independent samples; treating them as such inflates the effective sample size and the reported significance. Please report statistics per unique viewpoint or per phantom (n = 5 per phantom), or use an analysis that accounts for the repeated-measures structure, and state the number of independent cases explicitly.
  2. [§3.2 and §3.4] The partial point clouds used to obtain the strong TRE reductions in Table 2 are generated by the same pipeline at training and test time: posterior cropping, downsampling to D points, and selecting the N nearest points to a viewpoint. The in vitro test uses the same five-viewpoint procedure, so the 30% visibility condition is exactly the distribution the network was trained on. The Discussion concedes that real intra-operative data may contain noise, holes, and occlusions not represented in this simulation. To support the claim that completion improves registration in the targeted clinical setting, the authors should evaluate on partial inputs produced by a different occlusion/noise model or on real intra-operative data, or explicitly delimit the claim to the matched synthetic occlusion setting.
  3. [§5 and Related Work] The paper positions its contribution relative to Jia et al. [5] and Foti et al. [2], stating that those methods require rigid initialization or manual correspondences, but it does not compare against them empirically. Since the central claim is that the proposed completion-based registration is an improvement over existing approaches, the absence of any such baseline makes the comparison claim unsubstantiated. At minimum, run the earlier methods on the same phantom data or include a learning-based registration baseline; otherwise soften the claim.
  4. [§3.1 and §4.2] The registration evaluation is limited to two in vitro phantoms (No. 1 and No. 3), and no real patient data are used. The two phantoms may not capture the anatomical and deformation variability needed to support the general statement that completion 'significantly enhance[s] registration performance.' Please add per-phantom variability, justify the choice of these two phantoms, and discuss how the results would transfer to other patients.
  5. [§3.1 and §5] The network is trained on deformations from a neo-Hookean FEM simulation with stiffness 2-5 kPa and forces up to 3 N, while the in vitro phantoms are deformed by physical wedges. The manuscript does not quantify the similarity between the simulated deformation space and the actual phantom deformations, so the statement in Section 5 that the model 'generalize[s] effectively to realistic deformations observed in in vitro phantoms' is supported only by indirect TRE measurements on completed surfaces. Provide a direct comparison (e.g., deformation magnitudes, surface displacement fields) or acknowledge this as an assumption.
minor comments (5)
  1. [§3.5, Eq. (1)] In the second term of the Chamfer Distance definition, 'min_{hat t in P}' should be 'min_{hat t in hat T}'; the set P is undefined.
  2. [§2.2] In the MISE description, the notation '313 voxels' and '323' should be formatted as '31^3 voxels' and '32^3' for readability.
  3. [Table 1] Several numerical entries have an errant space after the decimal point (e.g., '0 .47', '3 .51', '0 .86'); these formatting issues should be corrected.
  4. [Figure 3 caption] The caption refers to 'five target point clouds generated for each intra-operative liver model during testing,' but it is unclear whether the figure shows in silico or in vitro models; clarify how the five viewpoints are selected and reused in Section 3.4.
  5. [§3.4] The phrase '100 random SO(3) rotations within the range [-pi/2, pi/2]' is imprecise; specify whether the range applies to Euler angles or axis-angle parameters.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the reported TRE gains are measured on held-out physical phantoms using a completion network trained on simulated deformations; the shared viewpoint-crop protocol is a distributional assumption, not a circular reduction.

full rationale

The paper's central derivation chain is: (1) VN-OccNet is trained on in-silico deformed liver surfaces synthesized from a patient-specific model; (2) at test time, a partial target point cloud is passed through the trained network to produce occupancy probabilities, from which a mesh is extracted via MISE and Marching Cubes; (3) the completed mesh is registered to a source point cloud using Go-ICP; and (4) TRE is measured against fiducials on physical in-vitro phantoms. No step equates the predicted completed surface with the training input by construction. The in-vitro phantom surfaces are segmented from CT and were not used to fit the network weights; only the occupancy threshold c is tuned on the validation set, which is ordinary hyperparameter selection and does not force the reported registration errors. The fact that test-time partial clouds are generated with the same viewpoint-cropping procedure as training makes the evaluation in-distribution with respect to occlusion pattern, but that is an acknowledged generalization limitation stated in the Discussion and Future Work, not a mathematical identity between the completed output and the input. The self-citations to [12], [19], and [17] provide motivation and datasets, but they are not used as uniqueness theorems or as the derivation of the TRE values. Since there is no equation-to-equation reduction and no fitted parameter renamed as a prediction, there is no specific circular step to report.

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

The completion network introduces no new physical entities or free parameters beyond standard training hyperparameters. The main free parameters are the occupancy threshold, point cloud sizes, and augmentation range. The load-bearing assumptions are that the biomechanical simulation captures real deformation patterns and that the pre-operative model matches the intra-operative anatomy; these are domain assumptions the paper itself flags as limiting in Section 5.

free parameters (5)
  • Occupancy threshold c = 0.4
    Chosen based on validation dataset (Section 3.3) to determine inside/outside surface in MISE; affects mesh quality and hence registration.
  • Anterior surface downsample D = 1000
    Controls target point cloud density; D=1000 corresponds to about 30% visibility, used in both training and testing (Section 3.3).
  • Nearest-point count N = 300
    Number of surface points nearest to a random viewpoint used to form the target cloud (Section 3.3).
  • Random rotation range for augmentation = [-pi/2, pi/2]
    Range for Z-axis and SO(3) rotation modes (Section 3.2); chosen by hand.
  • Training epochs = 604
    Stopping point for training VN-OccNet (Section 3.3); chosen manually for convergence.
assumptions (5)
  • domain assumption Simulated deformations from a neo-Hookean FEM model (Young's modulus 2-5 kPa, Poisson's ratio 0.35, forces up to 3 N) are representative of real intra-operative liver deformations
    Used to generate all training data (Section 3.1); the paper acknowledges real data may have noise and occlusions not present in simulations (Section 5).
  • domain assumption The pre-operative model is an accurate geometric representation of the intra-operative anatomy
    Patient-specific training uses the pre-operative model as the base shape; if the pre-operative model is inaccurate, the completion will be biased.
  • domain assumption VN-OccNet's rotation-equivariant architecture provides robustness to arbitrary SO(3) rotations
    Relied on for the Z/SO(3) and SO(3)/SO(3) settings (Sections 3.2, 4.1), based on the properties claimed in Deng et al. [1].
  • standard math Go-ICP is a globally optimal and appropriate baseline for rigid registration
    Used as the registration algorithm (Sections 2.2, 4.2) and cited as robust to partial visibility [17].
  • domain assumption The in vitro gelatin phantoms are a valid testbed for registration
    Physical gelatin phantoms with wedge deformations (Section 3.1); assumed to behave like liver tissue for this evaluation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Toward Patient-specific Partial Point Cloud to Surface Completion for Pre- to Intra-operative Registration in Image-guided Liver Interventions." pith.science (2026). https://pith.science/paper/WTKNWI2J

@misc{pith2026250519518,
  author       = {Pith},
  title        = {Pith review of: Toward Patient-specific Partial Point Cloud to Surface Completion for Pre- to Intra-operative Registration in Image-guided Liver Interventions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WTKNWI2J}},
  note         = {Machine review of arXiv:2505.19518}
}
read the original abstract

Intra-operative data captured during image-guided surgery lacks sub-surface information, where key regions of interest, such as vessels and tumors, reside. Image-to-physical registration enables the fusion of pre-operative information and intra-operative data, typically represented as a point cloud. However, this registration process struggles due to partial visibility of the intra-operative point cloud. In this research, we propose a patient-specific point cloud completion approach to assist with the registration process. Specifically, we leverage VN-OccNet to generate a complete liver surface from a partial intra-operative point cloud. The network is trained in a patient-specific manner, where simulated deformations from the pre-operative model are used to train the model. First, we conduct an in-depth analysis of VN-OccNet's rotation-equivariant property and its effectiveness in recovering complete surfaces from partial intra-operative surfaces. Next, we integrate the completed intra-operative surface into the Go-ICP registration algorithm to demonstrate its utility in improving initial rigid registration outcomes. Our results highlight the promise of this patient-specific completion approach in mitigating the challenges posed by partial intra-operative visibility. The rotation equivariant and surface generation capabilities of VN-OccNet hold strong promise for developing robust registration frameworks for variations of the intra-operative point cloud.

Figures

Figures reproduced from arXiv: 2505.19518 by the authors.

Figure 1
Figure 1. Overview diagram of proposed pipeline: The target point cloud is input to the trained VN-OccNet to output occupancy points (magenta). An integrated mesh generation reconstructs a mesh from the predicted occupancy. The mesh vertices are extracted to represent the complete target point cloud and registered with a source point cloud. a complete estimate of the target point cloud, denoted as Tb, and subsequently perform… view at source ↗
Figure 2
Figure 2. No. 1 and No. 3 phantoms feature 53 and 176 fiducials, respectively. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Visualization of five target point clouds generated for each intra-operative liver model during testing. Each target is captured from a unique viewpoint, with different regions of the liver obscured to mimic varying intra-operative visibility. 3.3 VN-OccNet Implementation We employed the PyTorch implementation of the VN-OccNet by Deng et al. [1]. The anterior liver points were downsampled to D = 1000, and N = 300 ne… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Visualization of mesh reconstruction results: The first column displays the target input point clouds alongside the ground truth target mesh. The second column presents the complete target liver surfaces generated by OccNet and VN-OccNet. Qual￾itative results are shown…
Figure 5
Figure 5. Figure 5: The registration results are displayed for the source point cloud (purple) and the target point cloud (blue) for phantoms No. 1 and No. 3. For easy visualization, the source point cloud is fixed and the target point clouds are moving. The first column represents the in…
Figure 6
Figure 6. Figure 6: Visualization of target fiducial markers (blue) and registered source fiducial markers (purple) for in vitro phantom No 1 and No 3. The target point clouds utilized for registration w/o surface completion have 30% visibility, and the full point clouds are reconstructed…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

20 extracted references · 17 canonical work pages

  1. [5]

    In: ICASSP 2021-2021 IEEE Inter- national Conference on Acoustics, Speech and Signal Processing (ICASSP)

    Jia, M., Kyan, M.: Improving intraoperative liver registration in image-guided surgery with learning-based reconstruction. In: ICASSP 2021-2021 IEEE Inter- national Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1230–1234. IEEE (2021)

    work page 2021

  2. [2]

    Foti, S., Koo, B., Dowrick, T., Ramalhinho, J., Allam, M., Davidson, B., Stoy- anov, D., Clarkson, M.J.: Intraoperative liver surface completion with graph con- volutional vae. In: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis: Second International Work- shop, UNSURE 2020, and Third Intern...

    work page 2020

  3. [1]

    In: Proceedings 14 N

    Deng, C., Litany, O., Duan, Y., Poulenard, A., Tagliasacchi, A., Guibas, L.J.: Vec- tor neurons: A general framework for so (3)-equivariant networks. In: Proceedings 14 N. Poudel et al. of the IEEE/CVF International Conference on Computer Vision. pp. 12200–12209 (2021)

    work page 2021

  4. [3]

    Journal of medical imaging5(2), 021203–021203 (2018)

    Heiselman, J.S., Clements, L.W., Collins, J.A., Weis, J.A., Simpson, A.L., Gee- varghese, S.K., Kingham, T.P., Jarnagin, W.R., Miga, M.I.: Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery. Journal of medical imaging5(2), 021203–021203 (2018)

    work page 2018

  5. [4]

    Journal of Medical Imaging 11(1), 015001–015001 (2024)

    Heiselman, J.S., Collins, J.A., Ringel, M.J., Peter Kingham, T., Jarnagin, W.R., Miga, M.I.: The image-to-physical liver registration sparse data challenge: com- parison of state-of-the-art using a common dataset. Journal of Medical Imaging 11(1), 015001–015001 (2024)

    work page 2024

  6. [6]

    Surgical endoscopy29, 3338–3347 (2015)

    Kenngott, H.G., Wünscher, J., Wagner, M., Preukschas, A., Wekerle, A.L., Neher, P., Suwelack, S., Speidel, S., Nickel, F., Oladokun, D., et al.: Openhelp (heidel- berg laparoscopy phantom): development of an open-source surgical evaluation and training tool. Surgical endoscopy29, 3338–3347 (2015)

    work page 2015

  7. [7]

    The International Journal of Medical Robotics and Computer Assisted Surgery12(2), 158–178 (2016)

    Lin, B., Sun, Y., Qian, X., Goldgof, D., Gitlin, R., You, Y.: Video-based 3D recon- struction, laparoscope localization and deformation recovery for abdominal min- imally invasive surgery: a survey. The International Journal of Medical Robotics and Computer Assisted Surgery12(2), 158–178 (2016)

    work page 2016

  8. [8]

    In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques

    Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3D sur- face construction algorithm. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques. p. 163–169. SIGGRAPH ’87, Association for Computing Machinery, New York, NY, USA (1987). https://doi.org/10.1145/37401.37422, https://doi.org/10.1145/37401.37422

    arXiv 1987

Show all 20 references
  1. [9]

    In: Medical Imaging 2023: Image-Guided Procedures, Robotic Inter- ventions, and Modeling

    Merrell, K., Jackson, P., Simon, R., Linte, C.: Developing and evaluating the fi- delity of patient specific kidney emulating phantoms for image-guided intervention applications. In: Medical Imaging 2023: Image-Guided Procedures, Robotic Inter- ventions, and Modeling. vol. 124...

    2023

  2. [10]

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

    Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: Learning 3D reconstruction in function space. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4460–4470 (2019)

    2019

  3. [11]

    In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV 23

    Pfeiffer, M., Riediger, C., Leger, S., Kühn, J.P., Seppelt, D., Hoffmann, R.T., Weitz, J., Speidel, S.: Non-rigid volume to surface registration using a data- driven biomechanical model. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd Internati...

    2020

  4. [12]

    In: Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

    Poudel, N., Yang, Z., Merrell, K., Simon, R., Linte, C.A.: Evaluation of intraop- erative patient-specific methods for point cloud completion for minimally invasive liver interventions. In: Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling. vol...

    2025

  5. [13]

    International Journal of Computer Assisted Radiology and Surgery 13, 947–956 (2018)

    Robu, M.R., Ramalhinho, J., Thompson, S., Gurusamy, K., Davidson, B., Hawkes, D., Stoyanov, D., Clarkson, M.J.: Global rigid registration of ct to video in laparo- scopic liver surgery. International Journal of Computer Assisted Radiology and Surgery 13, 947–956 (2018)

    2018

  6. [14]

    https://doi.org/10.34788/0S3G-QD15, https://www.rit.edu/researchcomputing/

    Rochester Institute of Technology: Research computing services (2025). https://doi.org/10.34788/0S3G-QD15, https://www.rit.edu/researchcomputing/

    2025 doi

  7. [15]

    In- ternational Journal of Computer Vision128, 1162–1181 (2020)

    Stutz, D., Geiger, A.: Learning 3D shape completion under weak supervision. In- ternational Journal of Computer Vision128, 1162–1181 (2020)

    2020

  8. [16]

    IEEE transactions on pattern analysis and machine intelligence 38(11), 2241–2254 (2015)

    Yang, J., Li, H., Campbell, D., Jia, Y.: Go-ICP: A globally optimal solution to 3D icp point-set registration. IEEE transactions on pattern analysis and machine intelligence 38(11), 2241–2254 (2015)

    2015

  9. [17]

    arXiv preprint arXiv:2412.19328 (2024)

    Yang, Z., Heiselman, J.S., Han, C., Merrell, K., Simon, R., Linte, C., et al.: Resolv- ing the ambiguity of complete-to-partial point cloud registration for image-guided liver surgery with patches-to-partial matching. arXiv preprint arXiv:2412.19328 (2024)

    2024 arXiv

  10. [18]

    International journal of computer assisted radiology and surgery18(6), 1025–1032 (2023)

    Yang, Z., Simon, R., Linte, C.A.: Learning feature descriptors for pre-and intra- operative point cloud matching for laparoscopic liver registration. International journal of computer assisted radiology and surgery18(6), 1025–1032 (2023)

    2023

  11. [19]

    IEEE Transactions on Medical Imaging pp

    Yang, Z., Simon, R., Merrell, K., Linte, C.A.: Boundary constraint-free biomechanical model-based surface matching for intraoperative liver defor- mation correction. IEEE Transactions on Medical Imaging pp. 1–1 (2024). https://doi.org/10.1109/TMI.2024.3515632

    2024

  12. [20]

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

    Yu, X., Rao, Y., Wang, Z., Liu, Z., Lu, J., Zhou, J.: Pointr: Diverse point cloud completion with geometry-aware transformers. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 12498–12507 (2021)

    2021

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.