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REVIEW 4 major objections 5 minor 35 references

GrowSplat: Constructing Temporal Digital Twins of Plants with Gaussian Splats

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read GrowSplat claims a fixed multi-camera rig plus coarse-to-fine rigid registration of Gaussian-splat point clouds produces temporally consistent 4D models of plant growth.

desk verdict GrowSplat is an honest but unvalidated system paper: the non-rigid deformation in the problem statement is never estimated, and temporal consistency is shown only qualitatively. read the letter →

arxiv 2505.10923 v2 pith:K4TA7MPN submitted 2025-05-16 cs.RO cs.CV

Simeon Adebola , Shuangyu Xie , Chung Min Kim , Justin Kerr , Bart M. van Marrewijk , Mieke van Vlaardingen , Tim van Daalen , E.N. van Loo
show 4 more authors
Jose Luis Susa Rincon Eugen Solowjow Rick van de Zedde Ken Goldberg
This is my paper · ORCID
classification cs.ROcs.CV
keywords 4Dplantmodelingtemporaldigitaltwin3DGaussiansplattingpointcloudregistrationphenotypingFastGlobalColoredICPnon-rigidgrowth
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

GrowSplat sets out to turn ordinary multi-view photographs of a plant, taken day after day by a fixed camera rig, into a single coherent 4D model of the plant's development. The core move is to reconstruct each time step as a 3D Gaussian splat, extract its point cloud, and then align successive point clouds in a common frame using a coarse-to-fine registration pipeline: fast point feature histograms with Fast Global Registration, followed by Colored ICP. If the claim holds, plant phenotyping—measuring leaf angle, internode length, growth rate, and other traits—can be done non-destructively and at industrial scale, since the same scanned plant can be followed over its whole growth cycle. The paper demonstrates the pipeline on two species with 40 and 55 time steps respectively, with an average interval between scans of 1 to 2.5 days. It presents visual results and leaves quantitative trait evaluation as future work.

What carries the argument

The load-bearing mechanism is the two-stage rigid registration applied to Gaussian-splat point clouds. Fast point feature histograms (FPFH), a local geometric descriptor invariant to rigid motion, provide the coarse feature matches; Fast Global Registration turns those matches into an initial rigid transform; Colored ICP refines the transform using both geometry and color. The 3D Gaussian splat is the representation that makes the point clouds dense and detailed enough for FPFH-based correspondence search to work.

What would settle it

Take two scans of the same plant two to three days apart, run GrowSplat, and measure the residual nearest-neighbour distance between registered point clouds separately for young leaves and for the main stem; if leaf residuals are systematically larger and grow with time, the rigid-registration assumption is failing at the leaf level. A second check is to label a leaf tip in the earlier cloud and see whether its registered position lands within a leaf of the later cloud.

Watch

Extended reading notes

Core claim

The central claim is that a complete 4D digital twin of a growing plant can be assembled from a sequence of 3D Gaussian splat reconstructions, one per observation day, aligned into a common plant frame by a coarse-to-fine rigid registration pipeline. For each time step the method trains a Gaussian splat from the 15 multi-view images, filters and downsamples the resulting splats, computes fast point feature histograms, runs Fast Global Registration with RANSAC rejection for coarse alignment, and refines with Colored ICP. The paper argues the result is a temporally consistent sequence of point clouds and rendered views that captures growth of Sequoia (40 time steps) and Quinoa (55 time steps) well enough to support non-destructive, longitudinal phenotyping. This turns the temporal plant modeling problem into a pipeline that runs on industrial-scale data from a fixed multi-camera setup.

Load-bearing premise

The method assumes rigid alignment alone can put successive scans of a growing plant into a common frame, even though leaves expand and stems bend between scans, and the paper does not measure how large those non-rigid changes are relative to the alignment error.

Editorial extensions

If this is right

  • If GrowSplat's alignment holds, breeders can extract longitudinal trait measurements like leaf angle and internode length directly from registered 4D models, replacing manual and destructive sampling.
  • Because the pipeline uses fast Gaussian-splat training and a fixed multi-camera rig, it can be rolled out to scan many plants in sequence, making high-throughput phenotyping practical.
  • The discrete time-step 4D model can serve as the backbone for downstream tasks such as biomass estimation, leaf detection, and growth-rate analysis.
  • Adding a new observation day reduces to training one splat and running registration, so the 4D model can be updated incrementally over a full growth cycle.
  • The method's dependence on rigid registration implies it will work best when scans are close enough in time (average under about three days) that growth between steps is small.

Reading between the lines

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

  • In my reading, the biggest open question the paper does not settle is whether the rigid alignment actually tracks leaves through time: if a young leaf grows more than the alignment tolerance between two scans, the 'same' leaf may appear as a different structure in the later cloud, and this could be tested with leaf-level correspondences.
  • One natural extension, implied but not implemented, is to estimate the deformation field from the problem statement using non-rigid registration or a learned growth model; that would turn the visual temporal twin into one with stable leaf identity.
  • If the registration is as consistent as the visuals suggest, interpolating between registered time steps would yield smoother growth trajectories and could fill in days when the plant was not scanned, something the paper lists as future work.
  • The paper's own Section VII says biological priors such as monotonic leaf growth and quantitative evaluations are future work; until those exist, 'temporal consistency' remains a visual property rather than a measured one.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper presents GrowSplat, a pipeline for constructing temporal digital twins of plants from multi-view images captured by the NPEC Maxi-Marvin system. At each time step, a 3D Gaussian Splatting model is trained using Splatfacto-MCMC, and the resulting point clouds are aligned across time using FPFH-based Fast Global Registration followed by Colored ICP. The method is demonstrated on two datasets (Sequoia, 40 steps; Quinoa, 55 steps) with qualitative results only. The paper states that quantitative evaluation is future work.

Significance. If the claimed temporal consistency were validated, the system could enable non-destructive, high-throughput plant phenotyping. The integration of 3DGS with existing registration tools is straightforward, and the paper does not introduce new algorithmic components. The significance is currently limited by the absence of any quantitative evaluation, baselines, or ablations, and by the mismatch between the stated non-rigid registration problem and the rigid-only method. The qualitative results are suggestive but not sufficient to establish the central claim.

major comments (4)
  1. [Section VII, Limitations and Future Work] The manuscript provides no quantitative evaluation: no registration error, no reconstruction quality metric, no comparison against baselines, and no ablation of the filtering or registration steps. Section VII explicitly states that 'We will also carry out quantitative evaluations of GrowSplat', which confirms that the central claim of a 'consistent 4D model' is not yet supported. This is a load-bearing omission that prevents verification of the paper's main contribution.
  2. [Section IV-C, Eqs. (1)-(4); Section V-B] The problem statement in Eq. (1) defines the transformation as T_tk(P_tk) = T_tk P_tk + d_tk, and Problem 1 optimizes over both T_1:K and d_1:K subject to the constraint d_tk^T d_tk ≤ β. However, the proposed method in Section V-B recovers only the rigid SE(3) component via FPFH/FGR and Colored ICP; no step estimates the non-rigid deformation field d_tk, and no experiment measures the residual error caused by unmodeled growth. At the given average intervals (2.4 and 1.4 days), leaf expansion and stem bending can be significant. The paper does not test the assumption that rigid alignment alone yields a coherent 4D model, so the central claim is unsupported.
  3. [Section III-B] The conversion of the division-model radial distortion coefficient κ to Nerfstudio's polynomial model is stated as K1 = -κ/√(w^2+h^2), K2 = K1^2, without derivation or validation. If this approximation is inaccurate, the reconstructed 3D point clouds will contain systematic geometric distortion, which propagates into the registration and final temporal model. The paper should justify this conversion or validate it with reprojection error or equivalent.
  4. [Section V-A, V-B, and IV-C] The method relies on several free parameters (filtering thresholds, lighting factor, α and β in constraints (3)-(4)), but their values are never specified, and the constraints (3)-(4) are not enforced in the algorithm. This makes the method difficult to reproduce and means the claimed solution to Problem 1 is not actually implemented. Additionally, no code or data are released for independent verification.
minor comments (5)
  1. [Section IV-C, Eq. (1)] The notation T_tk is used both as a function and as a matrix; this is confusing. Consider using distinct symbols, e.g., M_tk for the matrix and f_tk for the function.
  2. [Section V-A] The phrase 'we use segmentation masks that segment the plants from the rest of the image' is redundant; simpler phrasing would improve readability.
  3. [Section V-A] The 'lighting factor' is mentioned but is never defined or quantified; please provide details on its value and how it is applied.
  4. [Figure 2 caption] The word 'consists' is misspelled as 'consits' in the caption.
  5. [Table I] It would be helpful to include the number of images per time step and the image resolution, as these directly affect reconstruction quality and the feasibility of the approach.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reconstruction and registration pipeline is self-contained, and no predicted quantity is fitted to the claimed 4D output.

full rationale

GrowSplat's derivation chain is self-contained and does not reduce any claimed result to its own inputs. The pipeline is: train 3D Gaussian Splat reconstructions from multi-view images at each time step; filter splats and compute FPFH features; perform coarse Fast Global Registration; refine with Colored ICP; then render the temporally ordered sequence. Each stage uses standard, externally developed algorithms (3DGS, FPFH, FGR, ICP), and the temporal twin is the composition of these operations rather than a fitted value. The paper does not fit any parameter to the claimed 4D consistency; the alpha and beta constraints in Problem 1 are stated but are not estimated from the data and are not used to force the output. Self-citations in the related-work section, such as Nerfstudio, Dex-NeRF, and Evo-NeRF, are tooling citations and are not load-bearing for the central temporal-alignment claim. Section VII candidly states that quantitative evaluations and biological-prior constraints are future work; this is an evaluation gap, not circular reasoning. The known limitation that the method estimates only the rigid component T_tk and never the non-rigid deformation field d_tk from Eq. (1) is a correctness or validation concern about whether rigid alignment suffices at the observed growth intervals, but it does not make the output definitionally equal to the input. No equation in the paper is equivalent, by construction, to the result it is used to support, and no fitted parameter is renamed as a prediction. Therefore no circular step is present.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on domain assumptions about calibration, temporal proximity, and the validity of splat-derived point clouds, plus several hand-chosen but unspecified parameters (filtering thresholds, lighting factor, alpha, beta). No new physical entities are introduced.

free parameters (4)
  • Consecutive-scan rigid bound alpha = not reported / not used in experiments
    Defined in Problem 1 (Equation 3) to bound the change in rigid transformation between time steps, but the paper never assigns a value or verifies the constraint during registration.
  • Growth deformation bound beta = not reported / not used
    Bounded the non-rigid deformation field d_tk in Equation 4, but d_tk is never estimated, so beta is unused.
  • Splat filtering thresholds (log-scale range, scale ratio, quaternion norm) = not reported
    Section V-B.1 discards splats with 'excessively large or small scales', 'overly elongated splats', and checks quaternion norms, but the thresholds are not specified.
  • Lighting factor = not defined
    Section V-A says 'we also introduce a lighting factor' to compensate for darker images, but no formula or value is given.
assumptions (5)
  • domain assumption Maxi-Marvin camera poses and intrinsics are accurately calibrated and remain fixed across all time steps.
    Section III-A states cameras are calibrated and maintain position; the reconstruction and registration depend on this.
  • domain assumption Growth between consecutive observations is small (Avg(Delta t) < 3 days) and environmental conditions are similar, so scans differ mainly by small rigid and non-rigid deformations.
    Assumptions a.1 and a.2 in Section IV-A; the registration pipeline relies on temporal proximity for FGR and ICP initialization.
  • domain assumption Point clouds extracted from Gaussian splats after filtering faithfully represent plant geometry and support FPFH descriptor matching.
    Section V-B.1 assumes the filtered splat point clouds are 'physically valid and well-conditioned' and that FPFH features are discriminative for matching.
  • domain assumption Growth can be decomposed into a global rigid transform plus a non-rigid deformation field (Equation 1).
    Section IV-C formulates the transformation model; the method, however, only estimates the rigid part.
  • ad hoc to paper Division-model radial distortion can be approximated by the first two coefficients of Nerfstudio's polynomial model using K1 = -kappa/sqrt(w^2+h^2), K2 = K1^2.
    Section III-B states this conversion without derivation or validation; it is a modeling choice specific to this paper.

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Cite this review

Pith. "Pith review of GrowSplat: Constructing Temporal Digital Twins of Plants with Gaussian Splats." pith.science (2026). https://pith.science/paper/K4TA7MPN

@misc{pith2026250510923,
  author       = {Pith},
  title        = {Pith review of: GrowSplat: Constructing Temporal Digital Twins of Plants with Gaussian Splats},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K4TA7MPN}},
  note         = {Machine review of arXiv:2505.10923}
}
read the original abstract

Accurate temporal reconstructions of plant growth are essential for plant phenotyping and breeding, yet remain challenging due to complex geometries, occlusions, and non-rigid deformations of plants. We present a novel framework for building temporal digital twins of plants by combining 3D Gaussian Splatting with a robust sample alignment pipeline. Our method begins by reconstructing Gaussian Splats from multi-view camera data, then leverages a two-stage registration approach: coarse alignment through feature-based matching and Fast Global Registration, followed by fine alignment with Iterative Closest Point. This pipeline yields a consistent 4D model of plant development in discrete time steps. We evaluate the approach on data from the Netherlands Plant Eco-phenotyping Center, demonstrating detailed temporal reconstructions of Sequoia and Quinoa species. Videos and Images can be seen at https://berkeleyautomation.github.io/GrowSplat/

Figures

Figures reproduced from arXiv: 2505.10923 by the authors.

Figure 1
Figure 1. GrowSplat creates detailed 3D digital twins of plants with industrial￾scale data and then constructs temporal digital twins over time. advances facilities and tools: Ecotron, Plant-Microbe Inter￾action Phenotyping, Multi-Environment Climate Chambers, High-Throughput Phenotyping Climate Chambers, Green￾House Phenotyping, Open-Field Phenotyping, and Data[3]. Data collected from experiments using these modules al￾low f… view at source ↗
Figure 2
Figure 2. Maxi-Marvin is an indoor system for plant phenotyping that constists of 15 calibrated static cameras. Plants are moved into the Maxi￾Marvin using a conveyor belt and 15 images are taken. The system can be used for large plants up to a height of 70cm. a.2 Plant scans are performed under similar environmental conditions with minimal external disturbances. These assumptions are met by the system as referenced in the pr… view at source ↗
Figure 3
Figure 3. GrowSplat Digital Twins: Presented here are a side view for two different plants. Each column shows an RGB view of the 3D model [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: GrowSplat Digital Twins: Presented here are ten reconstructions of Quinoa across a period. The plant’s growth is seen over time. and we utilize a loss minimizing total rendered opacity. We also utilize initial pointclouds provided by NPEC. So our reconstruction is aide…
Figure 5
Figure 5. Figure 5: GrowSplat Digital Twins: Presented here are seven reconstructions of Sequoia across a period. The plant’s growth is seen over time. GeForce RTX 4090 GPU and 64.0GB Memory. Some examples of 3D gaussian splats can be found in [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 7
Figure 7. Figure 7: An example registration between two timestamps of Sequoia. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Top row: registration result for Sequoia. Bottom row: registration [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

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Works this paper leans on

35 extracted references · 27 canonical work pages

  1. [1]

    About NPEC

    NPEC, “About NPEC.” [Online]. Available: https://www.npec.nl/ about-npec/

    work page

  2. [2]

    High-throughput plant phenotyping platform (ht3p) as a novel tool for estimating agronomic traits from the lab to the field,

    D. Li, C. Quan, Z. Song, X. Li, G. Yu, C. Li, and A. Muhammad, “High-throughput plant phenotyping platform (ht3p) as a novel tool for estimating agronomic traits from the lab to the field,” Frontiers in Bioengineering and Biotechnology , vol. 8, 2021. [Online]. Available: https: //www.frontiersin.org/journals/bioengineering-and-biotechnology/ articles/10....

    work page arXiv 2021

  3. [3]

    Phenotyping modules

    NPEC, “Phenotyping modules.” [Online]. Available: https://www. npec.nl/phenotyping-modules/ Accepted to the Novel Approaches for Precision Agriculture and Forestry with Autonomous Robots IEEE ICRA Workshop - 2025

    work page 2025

  4. [4]

    Field robot for high-throughput and high-resolution 3d plant phenotyping: Towards efficient and sustainable crop production,

    F. Esser, R. A. Rosu, A. Cornelißen, L. Klingbeil, H. Kuhlmann, and S. Behnke, “Field robot for high-throughput and high-resolution 3d plant phenotyping: Towards efficient and sustainable crop production,” IEEE Robotics & Automation Magazine , vol. 30, no. 4, pp. 20–29, 2023

    work page 2023

  5. [5]

    A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping,

    T. Gill, S. K. Gill, D. K. Saini, Y . Chopra, J. P. de Koff, and K. S. Sandhu, “A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping,” Phenomics, vol. 2, no. 3, pp. 156–183, 2022

    work page 2022

  6. [6]

    High-throughput phenotyping: Breaking through the bottleneck in future crop breeding,

    P. Song, J. Wang, X. Guo, W. Yang, and C. Zhao, “High-throughput phenotyping: Breaking through the bottleneck in future crop breeding,” The Crop Journal , vol. 9, no. 3, pp. 633–645, 2021, rice as a model crop: genetics, genomics and breeding. [Online]. Available: https: //www.sciencedirect.com/science/article/pii/S2214514121000829

    work page 2021

  7. [7]

    Automated pruning and irrigation of polyculture plants,

    S. Adebola, M. Presten, R. Parikh, S. Aeron, S. Mukherjee, S. Sharma, M. Theis, W. Teitelbaum, E. Solowjow, and K. Goldberg, “Automated pruning and irrigation of polyculture plants,” IEEE Transactions on Automation Science and Engineering , vol. 21, no. 3, pp. 2199–2210, 2024

    work page 2024

  8. [8]

    Plant phenotyping: an active vision cell for three- dimensional plant shoot reconstruction,

    J. A. Gibbs, M. Pound, A. P. French, D. M. Wells, E. Murchie, and T. Pridmore, “Plant phenotyping: an active vision cell for three- dimensional plant shoot reconstruction,” Plant physiology , vol. 178, no. 2, pp. 524–534, 2018

    work page 2018

Show all 35 references
  1. [9]

    Measuring crops in 3d: using geometry for plant pheno- typing,

    S. Paulus, “Measuring crops in 3d: using geometry for plant pheno- typing,” Plant methods , vol. 15, no. 1, p. 103, 2019

    2019

  2. [10]

    How to make sense of 3d representations for plant phenotyping: a compendium of processing and analysis techniques,

    N. Harandi, B. Vandenberghe, J. Vankerschaver, S. Depuydt, and A. Van Messem, “How to make sense of 3d representations for plant phenotyping: a compendium of processing and analysis techniques,” Plant Methods , vol. 19, no. 1, p. 60, 2023

    2023

  3. [11]

    Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots,

    F. Magistri, R. Marcuzzi, E. Marks, M. Sodano, J. Behley, and C. Stachniss, “Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots,” in icra, 2024

    2024

  4. [12]

    Contrastive 3d shape completion and reconstruction for agricultural robots using rgb-d frames,

    F. Magistri, E. Marks, S. Nagulavancha, I. Vizzo, T. L ¨abe, J. Behley, M. Halstead, C. McCool, and C. Stachniss, “Contrastive 3d shape completion and reconstruction for agricultural robots using rgb-d frames,” ral, vol. 7, no. 4, pp. 10 120–10 127,

  5. [13]

    Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots,

    Y . Pan, F. Magistri, T. L¨abe, E. Marks, C. Smitt, C. McCool, J. Behley, and C. Stachniss, “Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023

    2023

  6. [14]

    3d move to see: Multi-perspective visual servoing towards the next best view within unstructured and occluded environments,

    C. Lehnert, D. Tsai, A. Eriksson, and C. McCool, “3d move to see: Multi-perspective visual servoing towards the next best view within unstructured and occluded environments,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019, pp. 3890–3897

    2019

  7. [15]

    Nerf: Representing scenes as neural radiance fields for view synthesis,

    B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoor- thi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in ECCV, 2020

    2020

  8. [16]

    Nerfstudio: A modular framework for neural radiance field development,

    M. Tancik, E. Weber, E. Ng, R. Li, B. Yi, J. Kerr, T. Wang, A. Kristoffersen, J. Austin, K. Salahi, A. Ahuja, D. McAllister, and A. Kanazawa, “Nerfstudio: A modular framework for neural radiance field development,” in ACM SIGGRAPH 2023 Conference Proceedings, ser. SIGGRAPH ’23, 2023

    2023

  9. [17]

    Dex-nerf: Using a neural radiance field to grasp transparent objects,

    J. Ichnowski, Y . Avigal, J. Kerr, and K. Goldberg, “Dex-nerf: Using a neural radiance field to grasp transparent objects,” in Proceedings of the 5th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol

  10. [18]

    Evo-nerf: Evolving nerf for sequential robot grasping of transparent objects,

    J. Kerr, L. Fu, H. Huang, Y . Avigal, M. Tancik, J. Ichnowski, A. Kanazawa, and K. Goldberg, “Evo-nerf: Evolving nerf for sequential robot grasping of transparent objects,” in Proceedings of The 6th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, ...

  11. [19]

    Language embedded radiance fields for zero- shot task-oriented grasping,

    A. Rashid, S. Sharma, C. M. Kim, J. Kerr, L. Y . Chen, A. Kanazawa, and K. Goldberg, “Language embedded radiance fields for zero- shot task-oriented grasping,” in 7th Annual Conference on Robot Learning, 2023. [Online]. Available: https://openreview.net/forum?id= k-Fg8JDQmc

    2023

  12. [20]

    Dis- tilled feature fields enable few-shot language-guided manipulation,

    W. Shen, G. Yang, A. Yu, J. Wong, L. P. Kaelbling, and P. Isola, “Dis- tilled feature fields enable few-shot language-guided manipulation,” in 7th Annual Conference on Robot Learning , 2023

    2023

  13. [21]

    3d gaussian splatting for real-time radiance field rendering,

    B. Kerbl, G. Kopanas, T. Leimk ¨uhler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics , vol. 42, no. 4, July 2023. [Online]. Available: https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

    2023

  14. [22]

    From nerfs to gaussian splats, and back,

    S. He, Z. Osman, and P. Chaudhari, “From nerfs to gaussian splats, and back,” arXiv preprint arXiv:2405.09717 , 2024

    2024 arXiv

  15. [23]

    Gaussiangrasper: 3d language gaussian splatting for open-vocabulary robotic grasping,

    Y . Zheng, X. Chen, Y . Zheng, S. Gu, R. Yang, B. Jin, P. Li, C. Zhong, Z. Wang, L. Liu, C. Yang, D. Wang, Z. Chen, X. Long, and M. Wang, “Gaussiangrasper: 3d language gaussian splatting for open-vocabulary robotic grasping,” 2024. [Online]. Available: https://arxiv.org/abs/2403.09637

    2024 arXiv

  16. [24]

    Analyzing growing plants from 4d point cloud data,

    Y . Li, X. Fan, N. J. Mitra, D. Chamovitz, D. Cohen-Or, and B. Chen, “Analyzing growing plants from 4d point cloud data,” ACM Transac- tions on Graphics (TOG) , vol. 32, no. 6, pp. 1–10, 2013

    2013

  17. [25]

    Spatio-temporal non-rigid registration of 3d point clouds of plants,

    N. Chebrolu, T. L ¨abe, and C. Stachniss, “Spatio-temporal non-rigid registration of 3d point clouds of plants,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 3112–3118

    2020

  18. [26]

    3D imaging – the Maxi-MARVIN

    NPEC, “3D imaging – the Maxi-MARVIN.” [Online]. Available: https://www.npec.nl/tool/3d-imaging-the-maxi-marvin/

  19. [27]

    calibrate cameras [HALCON Operator Reference / Version 13.0.4]

    Halcon, “calibrate cameras [HALCON Operator Reference / Version 13.0.4].” [Online]. Available: https://www.mvtec.com/doc/halcon/13/ en/calibrate cameras.html

  20. [28]

    nerfstudio.cameras.camera utils - nerfstudio

    Nerfstudio, “nerfstudio.cameras.camera utils - nerfstudio.” [On- line]. Available: https://docs.nerf.studio/ modules/nerfstudio/cameras/ camera utils.html#get distortion params

  21. [29]

    Mathematical supplement for the gsplat library,

    V . Ye and A. Kanazawa, “Mathematical supplement for the gsplat library,” 2023

    2023

  22. [30]

    3d gaussian splatting as markov chain monte carlo,

    S. Kheradmand, D. Rebain, G. Sharma, W. Sun, Y .-C. Tseng, H. Isack, A. Kar, A. Tagliasacchi, and K. M. Yi, “3d gaussian splatting as markov chain monte carlo,” in Advances in Neural Information Pro- cessing Systems (NeurIPS) , 2024, spotlight Presentation

    2024

  23. [31]

    Fast point feature histograms (fpfh) for 3d registration,

    R. B. Rusu, N. Blodow, and M. Beetz, “Fast point feature histograms (fpfh) for 3d registration,” in 2009 IEEE international conference on robotics and automation . IEEE, 2009, pp. 3212–3217

    2009

  24. [32]

    Fast global registration,

    Q. Zhou, J. Park, and V . Koltun, “Fast global registration,” inComputer Vision - 14th European Conference, ECCV 2016, Proceedings , ser. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), B. Le...

    2016

  25. [164]

    PMLR, 08–11 Nov 2022, pp. 526–536. [Online]. Available: https://proceedings.mlr.press/v164/ichnowski22a.html

    2022

  26. [205]

    PMLR, 14–18 Dec 2023, pp. 353–367. [Online]. Available: https://proceedings.mlr.press/v205/kerr23a.html

    2023

  27. [2022]

    Available: https://www.ipb.uni-bonn.de/wp-content/ papercite-data/pdf/magistri2022ral-iros.pdf

    [Online]. Available: https://www.ipb.uni-bonn.de/wp-content/ papercite-data/pdf/magistri2022ral-iros.pdf

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