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REVIEW 3 major objections 5 minor 104 references

DA-NBV: A Direction-Aware Next-Best-View Planner for Efficient 3D Reconstruction of Ships at Sea

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read DA-NBV records each voxel's viewing-direction history and uses it to pick viewpoints, achieving 98.49% coverage and 43% lower Chamfer distance than the best baseline.

desk verdict Solid direction-aware NBV idea with clean ablations, but the headline gains are only demonstrated in an unvalidated simulator with no error bars or released artifacts. read the letter →

arxiv 2608.08025 v1 pith:4IETSLVH submitted 2026-08-08 cs.RO cs.AI

Jiaming Chen , Juntao Yang , Zhentao Zou , Qi Ming , Yi Yu , Zhihang Zhong , Xue Yang , Xue Jiang
show 1 more author
Yue Zhou
This is my paper · ORCID
classification cs.ROcs.AI
keywords next-best-viewplanning3DreconstructiondirectionalcoveragemaritimeUAVscanningreinforcementlearningPositionAdvantageFieldsea-statesimulationship
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 tries to establish that replacing an occupancy-only reconstruction state with one that also records, for each voxel, which directions it has been seen from makes an automated drone scan of a ship at sea both more complete and cheaper to execute—a concern for maritime supervision and damage assessment, where manual flight is costly. DA-NBV accumulates per-voxel viewing-direction vectors, encodes the missing directions into a learnable Position Advantage Field, and pairs this with a locally constrained autoregressive action policy and a convex coverage-shaping reward. On the paper's maritime simulator with 300 ship models, the policy reaches a 98.49% reconstruction coverage rate versus 95.53% for the strongest baseline, cuts Chamfer distance from 6.49 cm to 3.68 cm, and retains most of its margin when the sea state is raised from 0 to 9. If the simulator is a faithful proxy, the result is evidence that directional coverage is a missing state variable for efficient, high-quality scanning of self-occluding objects.

What carries the argument

The mechanism that carries the argument is the Position Advantage Field (PAF), a learnable scalar field over a local lattice of candidate UAV positions. A Learnable Position Advantage Scorer evaluates each voxel–candidate pair from distance, approximate visibility transmittance, alignment with yet-unobserved direction bins, and PCA-based geometric complexity (linearity, scattering, and curvature), and sums the learned utilities over active voxels. The PAF tells the policy where missing directional coverage can be gained, while a two-stage action head first picks a local relative translation and then predicts yaw and pitch, and the reward uses a convex shaping $f(x)=x^3$ on directional-coverage increments so that late-game gains are not undervalued.

What would settle it

A field test on a real quadrotor over a known vessel under measured sea states, running DA-NBV with the paper's motion-compensation pipeline, would falsify the central claim if the resulting coverage rate and Chamfer distance do not reproduce the reported margin over Hestia.

Watch

Extended reading notes

Core claim

DA-NBV's central claim is that a voxel marked as observed is an insufficient planning signal for ships: the planner must also know whether its surface has been seen from enough complementary directions. The method therefore discretizes the viewing sphere into twelve directional bins per voxel, records which bins have been activated by past observations, and derives a Position Advantage Field that scores candidate UAV positions by how well they align with the directions still missing, weighted by local geometric complexity and an approximate visibility transmittance. Trained with proximal policy optimization in the wave-driven simulator, this state representation yields reconstruction completeness of 98.49% and Chamfer distance of 3.68 cm on SeaShip-3D, together with higher coverage per step and per path length than occupancy-based and voxel-face-based baselines.

Load-bearing premise

The load-bearing premise is that the simplified maritime simulator—an eight-wave ocean with waterline-derived heave, roll, and pitch, height-comparison sea occlusion, and scaled ship and wind parameters—faithfully predicts how a real UAV scan would perform at sea.

Editorial extensions

If this is right

  • An occupancy-only or voxel-face-only state systematically underestimates the reconstruction needs of self-occluding structures, so recording per-voxel direction history should improve completeness for any such object.
  • With ICP motion compensation, wave-induced heave, roll, and pitch are manageable: DA-NBV's coverage drops only 0.89 percentage points and Chamfer distance rises only 0.88 cm from Sea State 0 to Sea State 9.
  • The locally constrained action space and the nonlinear reward act on different components—path efficiency versus directional completeness—and the complete system obtains both, implying the design choices are complementary rather than redundant.
  • Reported cross-dataset results on Houses3K and OmniObject3D show the directional state transfers to static objects, so the method is not overfit to ship geometry.

Reading between the lines

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

  • An untested step the paper leaves open is measuring how much of the gain depends on the approximate visibility model (axis-aligned bounding-box transmittance and height-comparison sea occlusion) by comparing with exact ray tracing in a subset of scenes.
  • The directional state could be combined with appearance-based quality signals, so a natural next reward would penalize reprojection error or texture fidelity, since geometric coverage alone does not guarantee surfaces usable for photogrammetric measurement.
  • The readiest deployment test is to treat the learned termination action as the stopping rule under a fixed battery budget and measure reconstruction quality per unit flight time, which is closer to the real operating constraint than a fixed view count.
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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

3 major / 5 minor

Summary. DA-NBV proposes a direction-aware next-best-view planner for UAV-based 3D reconstruction of ships at sea. It extends occupancy-grid states with voxel-level directional observation bins, learns a Position Advantage Field (PAF) that scores candidate viewpoints from missing-direction alignment, visibility, distance, and geometric complexity, and couples this with a locally constrained autoregressive action policy and a convex coverage-shaping reward. Training and evaluation are conducted in an Isaac Gym simulator built on the new SeaShip-3D dataset, with wave-induced heave, roll, pitch, simplified sea-surface occlusion, and a wind field. The paper reports that DA-NBV improves coverage rate by about 3 percentage points, reduces Chamfer distance by 43%, and improves path efficiency relative to GenNBV and Hestia, with ablations attributing the gains to the PAF, the local action space, and the reward shaping.

Significance. The core idea is well motivated: occupancy-based NBV policies discard information about the diversity of viewing directions, which is clearly relevant for self-occluded ship superstructures. The PAF formulation, the local autoregressive action decomposition, and the coverage-shaping reward are coherent, and the ablations in Table 2 are directionally consistent with the stated roles of each component. The problem selection and the construction of a ship-oriented dataset with an accompanying maritime simulator are useful contributions if validated. However, the headline quantitative claims currently rest on a single simplified simulator whose fidelity is not established, and the headline numbers are reported as point estimates without uncertainty quantification. The paper also provides no code or simulator release, which hinders independent verification. With simulation-only claims and proper statistical reporting, the proposed components would be a clear incremental contribution; as written, the 'ships at sea' claim is stronger than the evidence supports.

major comments (3)
  1. [Maritime Simulation Environment and Dataset (Eqs. 28-35)] The evaluation environment is the sole evidence for the headline at-sea claim, and its scale-normalization assumptions are not validated. The paper scales all spatial quantities and airspeeds by eta_L while retaining the reference time scale through g_eq = eta_L g, but the UAV platform is modeled after the Crazyflie without scaling its mass, thrust, or drag. For a 100 m vessel normalized to 15 m, eta_L is about 0.15, so V_a becomes 3.75 m/s and the Sea State 9 reference winds of 20.8-24.4 m/s become about 3.1-3.7 m/s; this is a substantially different wind-to-airspeed and control-authority regime from a Crazyflie scanning a real 100 m ship. The four-point waterline response in Eqs. (32)-(34), the height-comparison sea-occlusion model, and the absence of GPS/IMU drift, gust transients, and registration failure modes mean that the measured 3 pp CR and 43% CD gains cannot be assumed to transfer to deployment. Please either validate the scaling and ship-response model against real ship/UAV data or explicitly restrict the central claim to simulation.
  2. [Performance Comparison and Experimental Protocol] The headline results in Table 1 and Table 3 are point estimates. The supplementary protocol evaluates 10 randomized episodes per test object, but no standard deviations, confidence intervals, or significance tests are reported, so it is unclear whether the 2.96 pp CR gap and the 43% CD reduction are stable across episodes or dominated by a few favorable runs. Please report per-metric distributions across the SeaShip-3D episodes, include pairwise effect sizes or significance tests, and adjust the abstract and conclusion claims accordingly.
  3. [Reward Design and Policy Optimization] The training reward uses ground-truth observability masks M(v,j) and ground-truth complexity weights c_v, as stated in the main text and detailed in the supplement, while the paper does not report whether the comparison baselines receive an equivalent privileged reward. Because the paper also states that all methods are trained under identical conditions, the reported gains may partly reflect an asymmetry in training signal rather than the proposed directional state and action space. Please either train the baselines with analogous ground-truth-derived rewards or report a DA-NBV ablation trained without the ground-truth reward quantities, so that the contribution of the proposed state/action/reward design can be isolated.
minor comments (5)
  1. [Experimental Setup] The text says all methods are evaluated with the same fixed number of views, but DA-NBV includes a stop action and terminal reward; please clarify whether the stop action is exercised during evaluation and how the fixed view budget is reconciled with a learned termination decision.
  2. [Experimental Protocol] The supplementary material states that 50 SeaShip-3D test ships and 10 episodes per ship are used for each sea state; please state the total number of episodes per metric to avoid ambiguity in the reader's interpretation of the reported values.
  3. [Visibility Approximation] The visibility approximation validation reports masking accuracy at a 0.10 threshold; it would be clearer to also report the effect of this approximation on final planning metrics, since the reported 13.4% false-mask rate is not directly connected to reconstruction quality.
  4. [General] No code, trained models, or simulator source are indicated as available; providing them would substantially improve reproducibility and allow independent verification of the baseline implementations.
  5. [Table 1 and Table 3] The 'All' columns average across dynamic maritime and static object datasets, which have different protocols; please justify this aggregate or present the datasets separately as the primary comparison.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: headline gains rest on external ground-truth metrics, privileged reward inputs are excluded from the policy, and there are no load-bearing self-citations.

full rationale

The central claim (DA-NBV improves CR, CD, and path efficiency) is measured against ground-truth point clouds and ground-truth surface meshes (Eq. 27 and CD via PyTorch3D), not against quantities produced by the learned scorer or the PAF. The reward uses privileged ground-truth complexity and observability masks, but the paper explicitly states these are used "exclusively for reward computation rather than as policy inputs," so they are not fitted parameters renamed as predictions. The PAF and LPAS are learned components optimized with PPO; their contributions are evaluated by ablations on held-out ships, and the headline CR/CD metrics are not the same function as the reward. The directional-coverage reward is aligned with the DCR ablation metric, but that is a training-objective choice rather than a circular reduction of the headline reconstruction-completeness claim. The comparisons to GenNBV, Hestia, Scan-RL, and ActiveRMAP are external baselines; no load-bearing argument reduces to a self-citation. The equivalent-gravity scaling in Eq. (31) is an internal coordinate-scaling choice that preserves wave phase and time scale by construction, but it does not encode the target result. The main caveat is external validity of the simplified maritime simulator (four-point ship response, height-comparison occlusion, scaled wind and airspeed); that is a testbed-fidelity concern, not a circular derivation.

Assumptions & free parameters 8 free parameters · 7 assumptions · 0 invented entities

The central claim is empirical, so most load-bearing content sits in design choices and simulation assumptions. The free parameters are discretization constants and reward or hyper-parameters that could be tuned to affect the reported gains. The axioms are the domain assumptions about direction diversity, ground-truth availability, and simulator fidelity. The paper introduces no new physical entities; its algorithmic constructs are captured as parameters and assumptions.

free parameters (8)
  • Directional bins N_d = 12
    Free design choice for discretizing the unit sphere; the paper says fewer or greater bins are worse but gives no quantitative ablation.
  • Coverage threshold tau = 4 cm
    Chosen from camera resolution 15 m / 400 px = 3.75 cm per pixel plus margin; affects reported CR values.
  • Reward weights lambda_dir, lambda_len, lambda_step = not reported
    Weights in Eq. (8) are not given numerically; they are presumably tuned during training.
  • Visibility masking threshold = 0.10
    Voxel contributions are masked when estimated visibility is below 0.10 in Eq. (10); validated only on synthetic visibility labels.
  • Reward shaping exponent = 3 (f(x)=x^3)
    Convex shaping selected because several convex functions outperform the linear formulation; the exponent is not derived.
  • Observation confidence constant kappa = 1
    Controls the confidence decay of online PCA descriptors in Eq. (19).
  • Reference airspeed V_a_ref = 25 m/s
    Set manually in the wind and flight-time model of Eq. (35).
  • Candidate lattice and action step = 1 m grid, +/-5 m range, 0.2 m step
    Defines the local action search resolution; chosen by hand and then scaled by ship normalization.
assumptions (7)
  • domain assumption Directional observation diversity improves reconstruction accuracy for geometrically complex regions.
    Core principle behind the PAF and coverage reward; supported by ablations but not independently derived.
  • domain assumption Ground-truth geometry is available for reward construction in training.
    M(v,j) and complexity weight c_v are computed from ground-truth meshes (Eq. 23 and Eq. 18); at deployment these are unavailable.
  • domain assumption The simplified maritime simulator with eight wave components and four-point waterline response is representative enough.
    Eqs. (29) to (34); no real ship trials are performed, so results may not transfer.
  • domain assumption ICP registration compensates ship motion accurately in simulation.
    Motion compensation uses ICP; if registration fails, ghost occupancy distorts the reconstruction state.
  • ad hoc to paper The axis-aligned bounding-box visibility approximation is adequate for planning.
    Eqs. (2) to (4); chosen for speed and validated only on synthetic visibility labels.
  • ad hoc to paper Equivalent-gravity scaling preserves temporal dynamics under length normalization.
    Eq. (31) sets g_eq = eta_L g; a modeling convenience used to keep the reference time scale.
  • standard math PPO and standard RL training assumptions hold.
    Policy optimization follows Schulman et al.; no convergence guarantee is provided.

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Cite this review

Pith. "Pith review of DA-NBV: A Direction-Aware Next-Best-View Planner for Efficient 3D Reconstruction of Ships at Sea." pith.science (2026). https://pith.science/paper/4IETSLVH

@misc{pith2026260808025,
  author       = {Pith},
  title        = {Pith review of: DA-NBV: A Direction-Aware Next-Best-View Planner for Efficient 3D Reconstruction of Ships at Sea},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4IETSLVH}},
  note         = {Machine review of arXiv:2608.08025}
}
read the original abstract

Accurate 3D reconstruction of ships at sea is important for maritime supervision, damage assessment, and autonomous maritime operations. Although 3D reconstruction has advanced considerably, high-quality data acquisition still largely relies on manually designed trajectories or skilled operators, resulting in high costs and limited scalability. Next-best-view (NBV) planning automates this process by selecting subsequent viewpoints based on the current state. However, existing NBV policies mainly model spatial occupancy while overlooking directional observation history. This limitation is particularly problematic for ships: their complex superstructures and severe self-occlusions require observations from multiple viewpoints, and insufficient directional coverage often yields incomplete reconstructions. These challenges are further amplified at sea, where wave-induced heave, roll, and pitch continuously alter the ship's pose and surface visibility. Meanwhile, wind disturbances and limited onboard power impose stricter requirements on scanning efficiency. To address these challenges, we propose DA-NBV, a direction-aware NBV policy that augments the conventional occupancy state with directional observation statistics. We introduce a learnable Position Advantage Field (PAF) that uses directional information to guide viewpoint selection. The policy further adopts a locally constrained action space and a nonlinear coverage-shaping reward to improve scanning efficiency. We also develop the ship-oriented SeaShip-3D dataset and a configurable sea-state simulation environment. Experiments under varying heave, roll, and pitch conditions show that DA-NBV improves reconstruction completeness by approximately 3 percentage points and reduces Chamfer distance by 43% while achieving higher path efficiency.

Figures

Figures reproduced from arXiv: 2608.08025 by the authors.

Figure 1
Figure 1. Comparison of conventional and DA-NBV scanning strategies. Conventional NBV methods are primarily designed [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of DA-NBV. Motion-compensated depth observations update the occupancy grid [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Qualitative comparison of reconstruction results. Both Hestia and DA-NBV achieve nearly complete reconstruction of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Comparison of different shaping functions for the [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Ground-truth complexity visualizations used for reward-side analysis. From top to bottom: representative high-quality [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: PCA-derived complexity maps computed from ground-truth surface points for the cross-category generalization data. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Examples from the SeaShip-3D dataset after waterline alignment and scale normalization. [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]

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Works this paper leans on

104 extracted references · 71 canonical work pages

  1. [1]

    Bajcsy, R. 1988. Active Perception. Proceedings of the IEEE, 76(8): 966--1005

    1988

  2. [2]

    J.; and McKay, N

    Besl, P. J.; and McKay, N. D. 1992. A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2): 239--256

    1992

  3. [3]

    Chen, X.; Li, Q.; Wang, T.; Xue, T.; and Pang, J. 2024. GenNBV : Generalizable Next-Best-View Policy for Active 3D Reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16436--16445

    2024

  4. [4]

    Connolly, C. I. 1985. The Determination of Next Best Views. In Proceedings of the IEEE International Conference on Robotics and Automation, 432--435

    1985

  5. [5]

    Delmerico, J.; Isler, S.; Sabzevari, R.; and Scaramuzza, D. 2018. A Comparison of Volumetric Information Gain Metrics for Active 3D Object Reconstruction. Autonomous Robots, 42(2): 197--208

    2018

  6. [6]

    Doumanoglou, A.; Kouskouridas, R.; Malassiotis, S.; and Kim, T.-K. 2016. Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3583--3592

    2016

  7. [7]

    Fan, H.; Su, H.; and Guibas, L. J. 2017. A Point Set Generation Network for 3D Object Reconstruction From a Single Image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 605--613

    2017

  8. [8]

    D.; Alterovitz, R.; Frahm, J.-M.; Oliva, J.; and Sengupta, R

    Frahm, N.; Zhao, D.; Beltran, A. D.; Alterovitz, R.; Frahm, J.-M.; Oliva, J.; and Sengupta, R. 2025. VIN-NBV : A View Introspection Network for Next-Best-View Selection for Resource-Efficient 3D Reconstruction. arXiv:2505.06219

    arXiv 2025

Show all 104 references
  1. [9]

    Giernacki, W.; Skwierczy \'n ski, M.; Witwicki, W.; Wro \'n ski, P.; and Kozierski, P. 2017. Crazyflie 2.0 Quadrotor as a Platform for Research and Education in Robotics and Control Engineering. In 2017 22nd International Conference on Methods and Models in Automation and Robo...

    2017

  2. [10]

    Gu \'e don, A.; Monasse, P.; and Lepetit, V. 2022. SCONE : Surface Coverage Optimization in Unknown Environments by Volumetric Integration. In Advances in Neural Information Processing Systems, volume 35

    2022

  3. [11]

    N.; Kapoor, A.; Joshi, N.; and Hilliges, O

    Hepp, B.; Dey, D.; Sinha, S. N.; Kapoor, A.; Joshi, N.; and Hilliges, O. 2018. Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility. In Computer Vision -- ECCV 2018, 455--472

    2018

  4. [12]

    Hepp, B.; Nie ner, M.; and Hilliges, O. 2018. Plan3D : Viewpoint and Trajectory Optimization for Aerial Multi-View Stereo Reconstruction. ACM Transactions on Graphics, 38(1): 1--17

    2018

  5. [13]

    M.; Bennewitz, M.; Stachniss, C.; and Burgard, W

    Hornung, A.; Wurm, K. M.; Bennewitz, M.; Stachniss, C.; and Burgard, W. 2013. OctoMap : An Efficient Probabilistic 3D Mapping Framework Based on Octrees. Autonomous Robots, 34(3): 189--206

    2013

  6. [14]

    Isler, S.; Sabzevari, R.; Delmerico, J.; and Scaramuzza, D. 2016. An Information Gain Formulation for Active Volumetric 3D Reconstruction. In Proceedings of the IEEE International Conference on Robotics and Automation, 3477--3484

    2016

  7. [15]

    Jiang, W.; Lei, B.; and Daniilidis, K. 2024. FisherRF : Active View Selection and Mapping with Radiance Fields Using Fisher Information. In Computer Vision -- ECCV 2024, 422--440

    2024

  8. [16]

    Jin, L.; Zhong, X.; Pan, Y.; Behley, J.; Stachniss, C.; and Popovi \'c , M. 2025. ActiveGS : Active Scene Reconstruction Using Gaussian Splatting. IEEE Robotics and Automation Letters, 10(5): 4866--4873

    2025

  9. [17]

    Kazhdan, M.; and Hoppe, H. 2013. Screened Poisson Surface Reconstruction. ACM Transactions on Graphics, 32(3): 29:1--29:13

    2013

  10. [18]

    Knapitsch, A.; Park, J.; Zhou, Q.-Y.; and Koltun, V. 2017. Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction. ACM Transactions on Graphics, 36(4): 78:1--78:13

    2017

  11. [19]

    Kriegel, S.; Bodenm \"u ller, T.; Suppa, M.; and Hirzinger, G. 2011. A Surface-Based Next-Best-View Approach for Automated 3D Model Completion of Unknown Objects. In Proceedings of the IEEE International Conference on Robotics and Automation, 4869--4874

    2011

  12. [20]

    Lee, S.; Chen, L.; Wang, J.; Liniger, A.; Kumar, S.; and Yu, F. 2022. Uncertainty Guided Policy for Active Robotic 3D Reconstruction Using Neural Radiance Fields. IEEE Robotics and Automation Letters, 7(4): 12070--12077

    2022

  13. [21]

    Liu, T.; Shen, R.; Lei, Z.; Huo, Y.; Zhao, J.; and Xu, X. 2022. Wind Resistance Aerial Path Planning for Efficient Reconstruction of Offshore Ship. International Journal of Digital Earth, 15(1): 1881--1904

    2022

  14. [22]

    Lu, C.-Y.; Zhuang, Z.; Le, N. T. T.; Xiao, D.; Chang, Y.-C.; Do, T.; Sridhar, S.; and Lin, C.-T. 2026. Hestia : Voxel-Face-Aware Hierarchical Next-Best-View Acquisition for Efficient 3D Reconstruction. In Proceedings of the IEEE/CVF Winter Conference on Applications of Compute...

    2026

  15. [23]

    R.; Song, S.; Malihi, S.; Saadatseresht, M.; and Gerke, M

    Maboudi, M.; Homaei, M. R.; Song, S.; Malihi, S.; Saadatseresht, M.; and Gerke, M. 2023. A Review on Viewpoints and Path Planning for UAV -Based 3-D Reconstruction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16: 5026--5048

    2023

  16. [24]

    Makoviychuk, V.; Wawrzyniak, L.; Guo, Y.; Lu, M.; Storey, K.; Macklin, M.; Hoeller, D.; Rudin, N.; Allshire, A.; Handa, A.; and State, G. 2021. Isaac Gym : High Performance GPU -Based Physics Simulation for Robot Learning. In Proceedings of the Neural Information Processing Sy...

    2021

  17. [25]

    I.; Taud, H.; Sucar, L

    Mendoza, M.; Vasquez-Gomez, J. I.; Taud, H.; Sucar, L. E.; and Reta, C. 2020. Supervised Learning of the Next-Best-View for 3D Object Reconstruction. Pattern Recognition Letters, 133: 224--231

    2020

  18. [26]

    P.; Tancik, M.; Barron, J

    Mildenhall, B.; Srinivasan, P. P.; Tancik, M.; Barron, J. T.; Ramamoorthi, R.; and Ng, R. 2020. NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis. In Computer Vision -- ECCV 2020, 405--421

    2020

  19. [27]

    Pan, X.; Lai, Z.; Song, S.; and Huang, G. 2022. ActiveNeRF : Learning Where to See with Uncertainty Estimation. In Computer Vision -- ECCV 2022, 230--246

    2022

  20. [28]

    M.; Aguilar, J

    Peralta, D.; Casimiro, J.; Nilles, A. M.; Aguilar, J. A.; Atienza, R.; and Cajote, R. 2020. Next-Best View Policy for 3D Reconstruction. In Computer Vision -- ECCV 2020 Workshops, 558--573

    2020

  21. [29]

    J.; and Moskowitz, L

    Pierson, W. J.; and Moskowitz, L. 1964. A Proposed Spectral Form for Fully Developed Wind Seas Based on the Similarity Theory of S. A. Kitaigorodskii. Journal of Geophysical Research, 69(24): 5181--5190

    1964

  22. [30]

    Pito, R. 1999. A Solution to the Next Best View Problem for Automated Surface Acquisition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(10): 1016--1030

    1999

  23. [31]

    Raffin, A.; Hill, A.; Gleave, A.; Kanervisto, A.; Ernestus, M.; and Dormann, N. 2021. Stable-Baselines3: Reliable Reinforcement Learning Implementations. Journal of Machine Learning Research, 22(268): 1--8

    2021

  24. [32]

    Ravi, N.; Reizenstein, J.; Novotny, D.; Gordon, T.; Lo, W.-Y.; Johnson, J.; and Gkioxari, G. 2020. Accelerating 3D Deep Learning with PyTorch3D . arXiv:2007.08501

    2020 arXiv

  25. [33]

    Roberts, M.; Shah, S.; Dey, D.; Truong, A.; Sinha, S.; Kapoor, A.; Hanrahan, P.; and Joshi, N. 2017. Submodular Trajectory Optimization for Aerial 3D Scanning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 5334--5343

    2017

  26. [34]

    Rudin, N.; Hoeller, D.; Reist, P.; and Hutter, M. 2022. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. In Faust, A.; Hsu, D.; and Neumann, G., eds., Proceedings of the 5th Conference on Robot Learning, volume 164 of Proceedings of Machine Lea...

    2022

  27. [35]

    u ller, H.; D \

    Schmid, K.; Hirschm \"u ller, H.; D \"o mel, A.; Grixa, I.; Suppa, M.; and Hirzinger, G. 2012. View Planning for Multi-View Stereo 3D Reconstruction Using an Autonomous Multicopter. Journal of Intelligent & Robotic Systems, 65(1--4): 309--323

    2012

  28. [36]

    Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; and Klimov, O. 2017. Proximal Policy Optimization Algorithms. arXiv:1707.06347

    2017 arXiv

  29. [37]

    R.; Roth, G.; and Rivest, J.-F

    Scott, W. R.; Roth, G.; and Rivest, J.-F. 2003. View Planning for Automated Three-Dimensional Object Reconstruction and Inspection. ACM Computing Surveys, 35(1): 64--96

    2003

  30. [38]

    M.; Gaggero, T.; Gaiotti, M.; and Rizzo, C

    Shah, F. M.; Gaggero, T.; Gaiotti, M.; and Rizzo, C. M. 2021. Condition Assessment of Ship Structure Using Robot Assisted 3D-Reconstruction. Ship Technology Research, 68(3): 129--146

    2021

  31. [39]

    Tao, Y.; Ong, D.; Murali, V.; Spasojevic, I.; Chaudhari, P.; and Kumar, V. 2025. RT-GuIDE : Real-Time Gaussian Splatting for Information-Driven Exploration. IEEE Robotics and Automation Letters, 10(11): 11594--11601

    2025

  32. [40]

    Thrun, S.; Burgard, W.; and Fox, D. 2005. Probabilistic Robotics. MIT Press

    2005

  33. [41]

    I.; Lopez-Damian, E.; and Sucar, L

    Vasquez-Gomez, J. I.; Lopez-Damian, E.; and Sucar, L. E. 2009. View Planning for 3D Object Reconstruction. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 4015--4020

    2009

  34. [42]

    Weinmann, M.; Jutzi, B.; Hinz, S.; and Mallet, C. 2015. Semantic Point Cloud Interpretation Based on Optimal Neighborhoods, Relevant Features and Efficient Classifiers. ISPRS Journal of Photogrammetry and Remote Sensing, 105: 286--304

    2015

  35. [43]

    Wu, T.; Zhang, J.; Fu, X.; Wang, Y.; Ren, J.; Pan, L.; Wu, W.; Yang, L.; Wang, J.; Qian, C.; Lin, D.; and Liu, Z. 2023. OmniObject3D : Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation. In Proceedings of the IEEE/CVF Conference on Compu...

    2023

  36. [44]

    Xiao, W.; Santa Cruz, R.; Ahmedt-Aristizabal, D.; Salvado, O.; Fookes, C.; and Lebrat, L. 2024. NeRF Director : Revisiting View Selection in Neural Volume Rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20742--20751

    2024

  37. [45]

    Xu, Z.; Jin, R.; Wu, K.; Zhao, Y.; Zhang, Z.; Zhao, J.; Gao, F.; Gan, Z.; and Ding, W. 2025. HGS-Planner : Hierarchical Planning Framework for Active Scene Reconstruction Using 3D Gaussian Splatting. In Proceedings of the IEEE International Conference on Robotics and Automatio...

    2025

  38. [46]

    Xue, S.; Dill, J.; Mathur, P.; Dellaert, F.; Tsiotras, P.; and Xu, D. 2024. Neural Visibility Field for Uncertainty-Driven Active Mapping. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 18122--18132

    2024

  39. [47]

    Yan, D.; Liu, J.; Quan, F.; Chen, H.; and Fu, M. 2023. Active Implicit Object Reconstruction Using Uncertainty-Guided Next-Best-View Optimization. IEEE Robotics and Automation Letters, 8(10): 6395--6402

    2023

  40. [48]

    Yuksel, C. 2015. Sample Elimination for Generating Poisson Disk Sample Sets. Computer Graphics Forum, 34(2): 25--32

    2015

  41. [49]

    Zeng, R.; Zhao, W.; and Liu, Y.-J. 2020. PC-NBV : A Point Cloud Based Deep Network for Efficient Next Best View Planning. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 7050--7057

    2020

  42. [50]

    Zhan, H.; Zheng, J.; Xu, Y.; Reid, I.; and Rezatofighi, H. 2022. ActiveRMAP : Radiance Field for Active Mapping and Planning. arXiv:2211.12656

    2022 arXiv

  43. [51]

    Zheleznyakova, A. L. 2020. Physically-Based Method for Real-Time Modelling of Ship Motion in Irregular Waves. Ocean Engineering, 195: 106686

    2020

  44. [52]

    Zhou, Q.-Y.; Park, J.; and Koltun, V. 2018. Open3D : A Modern Library for 3D Data Processing. arXiv:1801.09847

    2018 arXiv

  45. [53]

    Ian , title =

    Connolly, C. Ian , title =. Proceedings of the IEEE International Conference on Robotics and Automation , pages =. 1985 , doi =

    1985

  46. [54]

    IEEE Transactions on Pattern Analysis and Machine Intelligence , volume =

    Pito, Richard , title =. IEEE Transactions on Pattern Analysis and Machine Intelligence , volume =. 1999 , doi =

    1999

  47. [55]

    and Roth, Gerhard and Rivest, Jean-Fran

    Scott, William R. and Roth, Gerhard and Rivest, Jean-Fran. View Planning for Automated Three-Dimensional Object Reconstruction and Inspection , journal =. 2003 , doi =

    2003

  48. [56]

    Proceedings of the IEEE International Conference on Robotics and Automation , pages =

    Isler, Stefan and Sabzevari, Reza and Delmerico, Jeffrey and Scaramuzza, Davide , title =. Proceedings of the IEEE International Conference on Robotics and Automation , pages =. 2016 , doi =

    2016

  49. [57]

    Autonomous Robots , volume =

    Delmerico, Jeffrey and Isler, Stefan and Sabzevari, Reza and Scaramuzza, Davide , title =. Autonomous Robots , volume =. 2018 , doi =

    2018

  50. [58]

    Irving and Taud, Hind and Sucar, Luis Enrique and Reta, Carolina , title =

    Mendoza, Miguel and Vasquez-Gomez, J. Irving and Taud, Hind and Sucar, Luis Enrique and Reta, Carolina , title =. Pattern Recognition Letters , volume =. 2020 , doi =

    2020

  51. [59]

    Computer Vision -- ECCV 2020 Workshops , pages =

    Peralta, Daryl and Casimiro, Joel and Nilles, Aldrin Michael and Aguilar, Justine Aletta and Atienza, Rowel and Cajote, Rhandley , title =. Computer Vision -- ECCV 2020 Workshops , pages =. 2020 , doi =

    2020

  52. [60]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

    Chen, Xiao and Li, Quanyi and Wang, Tai and Xue, Tianfan and Pang, Jiangmiao , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

  53. [61]

    Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , month =

    Lu, Cheng-You and Zhuang, Zhuoli and Le, Nguyen Thanh Trung and Xiao, Da and Chang, Yu-Cheng and Do, Thomas and Sridhar, Srinath and Lin, Chin-Teng , title =. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , month =

  54. [62]

    2022 , eprint =

    Zhan, Huangying and Zheng, Jiyang and Xu, Yi and Reid, Ian and Rezatofighi, Hamid , title =. 2022 , eprint =

    2022

  55. [63]

    Ship Technology Research , volume =

    Shah, Faisal Mehmood and Gaggero, Tomaso and Gaiotti, Marco and Rizzo, Cesare Mario , title =. Ship Technology Research , volume =. 2021 , doi =

    2021

  56. [64]

    International Journal of Digital Earth , volume =

    Liu, Tao and Shen, Ruiqi and Lei, Zhengling and Huo, Yuchi and Zhao, Jiansen and Xu, Xiaogang , title =. International Journal of Digital Earth , volume =. 2022 , doi =

    2022

  57. [65]

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , volume =

    Maboudi, Mehdi and Homaei, Mohammad Reza and Song, Soohwan and Malihi, Shirin and Saadatseresht, Mohammad and Gerke, Markus , title =. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , volume =. 2023 , doi =

    2023

  58. [66]

    and McKay, Neil D

    Besl, Paul J. and McKay, Neil D. , title =. IEEE Transactions on Pattern Analysis and Machine Intelligence , volume =. 1992 , doi =

    1992

  59. [67]

    Computer Graphics Forum , volume =

    Yuksel, Cem , title =. Computer Graphics Forum , volume =. 2015 , doi =

    2015

  60. [68]

    Semantic Point Cloud Interpretation Based on Optimal Neighborhoods, Relevant Features and Efficient Classifiers , journal =

    Weinmann, Martin and Jutzi, Boris and Hinz, Stefan and Mallet, Cl\'. Semantic Point Cloud Interpretation Based on Optimal Neighborhoods, Relevant Features and Efficient Classifiers , journal =. 2015 , doi =

    2015

  61. [69]

    2017 , eprint =

    Schulman, John and Wolski, Filip and Dhariwal, Prafulla and Radford, Alec and Klimov, Oleg , title =. 2017 , eprint =

    2017

  62. [70]

    Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks , volume =

    Makoviychuk, Viktor and Wawrzyniak, Lukasz and Guo, Yunrong and Lu, Michelle and Storey, Kier and Macklin, Miles and Hoeller, David and Rudin, Nikita and Allshire, Arthur and Handa, Ankur and State, Gavriel , title =. Proceedings of the Neural Information Processing Systems Tr...

  63. [71]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

    Wu, Tong and Zhang, Jiarui and Fu, Xiao and Wang, Yuxin and Ren, Jiawei and Pan, Liang and Wu, Wayne and Yang, Lei and Wang, Jiaqi and Qian, Chen and Lin, Dahua and Liu, Ziwei , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

  64. [72]

    , title =

    Fan, Haoqiang and Su, Hao and Guibas, Leonidas J. , title =. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , month =

  65. [73]

    ACM Transactions on Graphics , volume =

    Knapitsch, Arno and Park, Jaesik and Zhou, Qian-Yi and Koltun, Vladlen , title =. ACM Transactions on Graphics , volume =. 2017 , doi =

    2017

  66. [74]

    and Tancik, Matthew and Barron, Jonathan T

    Mildenhall, Ben and Srinivasan, Pratul P. and Tancik, Matthew and Barron, Jonathan T. and Ramamoorthi, Ravi and Ng, Ren , title =. Computer Vision -- ECCV 2020 , pages =. 2020 , doi =

    2020

  67. [75]

    Journal of Machine Learning Research , volume =

    Raffin, Antonin and Hill, Ashley and Gleave, Adam and Kanervisto, Anssi and Ernestus, Maximilian and Dormann, Noah , title =. Journal of Machine Learning Research , volume =. 2021 , url =

    2021

  68. [76]

    Proceedings of the 5th Conference on Robot Learning , editor =

    Rudin, Nikita and Hoeller, David and Reist, Philipp and Hutter, Marco , title =. Proceedings of the 5th Conference on Robot Learning , editor =. 2022 , url =

    2022

  69. [77]

    Crazyflie 2.0 Quadrotor as a Platform for Research and Education in Robotics and Control Engineering , booktitle =

    Giernacki, Wojciech and Skwierczy. Crazyflie 2.0 Quadrotor as a Platform for Research and Education in Robotics and Control Engineering , booktitle =. 2017 , doi =

    2017

  70. [78]

    2018 , eprint =

    Zhou, Qian-Yi and Park, Jaesik and Koltun, Vladlen , title =. 2018 , eprint =

    2018

  71. [79]

    2020 , eprint =

    Ravi, Nikhila and Reizenstein, Jeremy and Novotny, David and Gordon, Taylor and Lo, Wan-Yen and Johnson, Justin and Gkioxari, Georgia , title =. 2020 , eprint =

    2020

  72. [80]

    Computer Vision -- ECCV 2022 , pages =

    Pan, Xuran and Lai, Zihang and Song, Shiji and Huang, Gao , title =. Computer Vision -- ECCV 2022 , pages =. 2022 , doi =

    2022

  73. [81]

    IEEE Robotics and Automation Letters , volume =

    Lee, Soomin and Chen, Le and Wang, Jiahao and Liniger, Alexander and Kumar, Suryansh and Yu, Fisher , title =. IEEE Robotics and Automation Letters , volume =. 2022 , doi =

    2022

  74. [82]

    Advances in Neural Information Processing Systems , volume =

    Gu. Advances in Neural Information Processing Systems , volume =. 2022 , doi =

    2022

  75. [83]

    2025 , eprint =

    Frahm, Noah and Zhao, Dongxu and Beltran, Andrea Dunn and Alterovitz, Ron and Frahm, Jan-Michael and Oliva, Junier and Sengupta, Roni , title =. 2025 , eprint =

    2025

  76. [84]

    Thrun, Sebastian and Burgard, Wolfram and Fox, Dieter , title =

  77. [85]

    ACM Transactions on Graphics , volume =

    Kazhdan, Michael and Hoppe, Hugues , title =. ACM Transactions on Graphics , volume =. 2013 , doi =

    2013

  78. [86]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

    Xue, Shangjie and Dill, Jesse and Mathur, Pranay and Dellaert, Frank and Tsiotras, Panagiotis and Xu, Danfei , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =. 2024 , doi =

    2024

  79. [87]

    Computer Vision -- ECCV 2024 , pages =

    Jiang, Wen and Lei, Boshu and Daniilidis, Kostas , title =. Computer Vision -- ECCV 2024 , pages =. 2024 , doi =

    2024

  80. [88]

    Proceedings of the IEEE , volume =

    Bajcsy, Ruzena , title =. Proceedings of the IEEE , volume =. 1988 , doi =

    1988

  81. [89]

    Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , pages =

    Vasquez-Gomez, Juan Irving and Lopez-Damian, Efrain and Sucar, Luis Enrique , title =. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , pages =. 2009 , doi =

    2009

  82. [90]

    A Surface-Based Next-Best-View Approach for Automated 3D Model Completion of Unknown Objects , booktitle =

    Kriegel, Simon and Bodenm. A Surface-Based Next-Best-View Approach for Automated 3D Model Completion of Unknown Objects , booktitle =. 2011 , doi =

    2011

  83. [91]

    Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , pages =

    Zeng, Rui and Zhao, Wang and Liu, Yong-Jin , title =. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , pages =. 2020 , doi =

    2020

  84. [92]

    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , month =

    Doumanoglou, Andreas and Kouskouridas, Rigas and Malassiotis, Sotiris and Kim, Tae-Kyun , title =. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , month =. 2016 , doi =

    2016

  85. [93]

    ACM Transactions on Graphics , volume =

    Hepp, Benjamin and Nie. ACM Transactions on Graphics , volume =. 2018 , doi =

    2018

  86. [94]

    Proceedings of the IEEE International Conference on Computer Vision (ICCV) , month =

    Roberts, Mike and Shah, Shital and Dey, Debadeepta and Truong, Anh and Sinha, Sudipta and Kapoor, Ashish and Hanrahan, Pat and Joshi, Neel , title =. Proceedings of the IEEE International Conference on Computer Vision (ICCV) , month =. 2017 , doi =

    2017

  87. [95]

    View Planning for Multi-View Stereo 3D Reconstruction Using an Autonomous Multicopter , journal =

    Schmid, Korbinian and Hirschm. View Planning for Multi-View Stereo 3D Reconstruction Using an Autonomous Multicopter , journal =. 2012 , doi =

    2012

  88. [96]

    IEEE Robotics and Automation Letters , volume =

    Yan, Dongyu and Liu, Jianheng and Quan, Fengyu and Chen, Haoyao and Fu, Mengmeng , title =. IEEE Robotics and Automation Letters , volume =. 2023 , doi =

    2023

  89. [97]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

    Xiao, Wenhui and Santa Cruz, Rodrigo and Ahmedt-Aristizabal, David and Salvado, Olivier and Fookes, Clinton and Lebrat, Leo , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =. 2024 , doi =

    2024

  90. [98]

    Proceedings of the IEEE International Conference on Robotics and Automation , pages =

    Xu, Zijun and Jin, Rui and Wu, Ke and Zhao, Yi and Zhang, Zhiwei and Zhao, Jieru and Gao, Fei and Gan, Zhongxue and Ding, Wenchao , title =. Proceedings of the IEEE International Conference on Robotics and Automation , pages =. 2025 , doi =

    2025

  91. [99]

    IEEE Robotics and Automation Letters , volume =

    Tao, Yuezhan and Ong, Dexter and Murali, Varun and Spasojevic, Igor and Chaudhari, Pratik and Kumar, Vijay , title =. IEEE Robotics and Automation Letters , volume =. 2025 , doi =

    2025

  92. [100]

    and Bennewitz, Maren and Stachniss, Cyrill and Burgard, Wolfram , title =

    Hornung, Armin and Wurm, Kai M. and Bennewitz, Maren and Stachniss, Cyrill and Burgard, Wolfram , title =. Autonomous Robots , volume =. 2013 , doi =

    2013

  93. [101]

    IEEE Robotics and Automation Letters , volume =

    Jin, Liren and Zhong, Xingguang and Pan, Yue and Behley, Jens and Stachniss, Cyrill and Popovi. IEEE Robotics and Automation Letters , volume =. 2025 , doi =

    2025

  94. [102]

    and Kapoor, Ashish and Joshi, Neel and Hilliges, Otmar , title =

    Hepp, Benjamin and Dey, Debadeepta and Sinha, Sudipta N. and Kapoor, Ashish and Joshi, Neel and Hilliges, Otmar , title =. Computer Vision -- ECCV 2018 , pages =. 2018 , doi =

    2018

  95. [103]

    and Moskowitz, Lionel , title =

    Pierson, Willard J. and Moskowitz, Lionel , title =. Journal of Geophysical Research , volume =. 1964 , publisher =

    1964

  96. [104]

    , title =

    Zheleznyakova, Alexandra L. , title =. Ocean Engineering , volume =. 2020 , publisher =

    2020

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.