CoCap: Coordinated motion Capture for multi-actor scenes in outdoor environments

Aditya Rauniyar; Micah Corah; Sebastian Scherer

REVIEW 5 major objections 5 minor 13 references

CoCap: Coordinated motion Capture for multi-actor scenes in outdoor environments

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read Coordinated conflict-based view planning lets multi-drone camera teams capture cluttered outdoor scenes nearly as well as if the drones never had to avoid one another.

desk verdict A legitimate CBS-for-view-planning extension that works in two simulated scenarios, but the headline claim outruns the evidence. read the letter →

arxiv 2412.20695 v1 pith:RGZTNLN3 submitted 2024-12-30 cs.RO

Aditya Rauniyar , Micah Corah , Sebastian Scherer This is my paper · ORCID

classification cs.RO

keywords multi-robotviewplanningmotioncaptureaerialcamerasconflict-basedsearchcoverageoptimizationocclusion-awareUAVcoordinationmulti-actorscenes

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

This paper tries to establish that a team of camera-equipped drones can capture multiple moving actors in cluttered outdoor scenes almost as well when they must avoid each other as when they are allowed to fly freely. The proposed approach, CoCap, adapts conflict-based search from multi-agent navigation to view planning: drones initially plan greedily, and only when two trajectories collide does the planner add a constraint and re-plan one drone, repeating until a conflict-free joint plan is found. In two simulated scenarios with obstacles, narrow passages, and heavy occlusion, CoCap reports higher total coverage reward than sequential priority-based planning and approaches the reward of unconstrained planning. The result matters because real outdoor motion capture needs multiple coordinated viewpoints for 4D pose reconstruction, and collision constraints should not force drones to give up those viewpoints.

What carries the argument

The central mechanism is a constraint tree whose nodes hold a joint robot trajectory, a set of inter-robot constraints, the accumulated pixel coverage over actor faces, and the corresponding reward. When the pairwise conflict check $\Psi$ finds a collision between two drones, the node splits into two children, each adding a constraint to one of the two drones and re-planning that drone with the coverage already collected by the other drones held fixed. The objective is the incremental coverage gain of Eq. (1), the sum over actor faces of square-root marginal pixel-density gains, which rewards diverse multi-view coverage. A low-level single-agent view search, guided by a coverage heuristic and a discount factor, replaces the expensive value-iteration solver and allows fast replanning under the new constraints.

What would settle it

Record the corridor and bottleneck scenarios with a photorealistic renderer or real drones, run CoCap and sequential planning on the same actor trajectories, and compare the resulting 4D pose reconstruction error (for example mean per-joint 3D error) rather than the coverage reward. If sequential planning produces equal or lower pose error despite lower reported coverage, the paper's central claim is falsified.

Watch

Extended reading notes

Core claim

CoCap claims that the main loss of coverage in multi-drone motion capture comes from fixing a priority order for drones and planning them one after another, so that later drones inherit all earlier constraints. By instead treating conflicts as they appear and re-planning only the involved drone, CoCap keeps the rewards of diverse viewpoints while ensuring inter-robot safety. Concretely, in the corridor scenario CoCap reaches a total scaled reward of 4662 against 4127 for sequential planning, and in the bottleneck scenario 5162 against 5053; in both cases it approaches the no-constraint upper bound. The paper also claims a single-agent view search that plans in about 2.7 seconds compared with 3981 seconds for value iteration, at a reward cost of 3922 versus 4662, making online replanning feasible.

Load-bearing premise

The claim depends on the assumption that the pixel-density coverage reward computed over cuboid actor faces is a faithful proxy for real motion-capture quality, and that a 2.5D height map correctly models occlusion; if either fails, the simulated reward advantage may not translate into better real-world pose reconstruction.

Editorial extensions

If this is right

In narrow, obstacle-dense scenes, CoCap closes most of the gap between sequential planning and the unconstrained upper bound, so collision avoidance need not be the dominant cost of multi-drone coverage.
CoCap's reward advantage appears in both tested scenarios, and the gap between CoCap and sequential planning widens while the drones are inside the cluttered region where conflicts are frequent.
The single-agent view search reduces planning time from thousands of seconds to a few seconds, which makes online replanning practical for real-time capture, with a measurable but modest reward loss.
Coordinating view planning through a constraint tree, rather than a fixed priority order, is shown to be a viable way to enforce inter-robot constraints in perception tasks.
The reported comparisons use the same coverage reward for all methods, so the improvement is attributed to the coordination mechanism rather than to a different objective.

Reading between the lines

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

The paper does not report how the constraint tree grows with more than four drones; a natural test is scaling to eight or more drones to see whether the first-conflict-free solution remains computationally feasible.
If the pixel-density proxy holds, the same constraint-tree coordination could be applied to other multi-camera perception tasks, such as outdoor 3D reconstruction or surveillance, by swapping in their coverage rewards.
The single-agent search's lack of lookahead suggests a testable extension: adding a limited-depth rollout for later high rewards could raise its reward without sacrificing its speed advantage.
Real deployment would need to handle communication latency and centralization; a decentralized variant that resolves local conflicts without a central tree is a direct next step that the paper leaves unexplored.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The genuinely new thing here is applying Conflict-Based Search to camera-view planning rather than just path planning. Previous multi-drone view planners use sequential or prioritized planning; CoCap instead builds a constraint tree over robot trajectories and re-plans individual agents to recover coverage reward lost to collision constraints. That is a real adaptation of CBS, and the paper clearly explains the algorithmic changes: a coverage reward as the node cost, greedy initialization, and a value-iteration low-level solver. The single-agent search variant is also useful, cutting compute from hundreds of seconds to 2.7 seconds, even if at some reward cost. Credit where due: the problem is well motivated, the formalization is careful, and the limitations paragraph is unusually candid about the cuboid actors, the 2.5D height map, the lack of real deployment, and the centralized-communication risk.

The soft spots are concentrated in the evidence for the central claim. Table I reports only two deterministic scenarios with no error bars, no repeated runs, and no code or data released. The abstract says CoCap "outperforms existing sequential planning methods," but in the corridor the Search variant scores 3922 versus Sequential-MDP's 4127, and the bottleneck improvement is only 5162 versus 5053. The unconstrained baseline, the "ideal outcome" CoCap is said to approach, never appears as a number in Table I; it only shows up in figures, so the gap being closed is never quantified. And the reward itself, inherited from GreedyPerspectives, is a pixel-density proxy that nobody has validated against actual 4D pose reconstruction quality. These are not fatal flaws—they are addressable with randomized trials, released artifacts, and a table that includes the unconstrained baseline—but they do mean the paper's strongest sentence goes beyond what the reported numbers support.

The citation pattern is fine. Reusing the GreedyPerspectives reward is not circular because the comparison is between coordination mechanisms under the same reward, not a fit to a prior result. The related work is honest about CBS being underexplored in perception planning.

Who is this for? Researchers working on multi-drone perception, especially anyone building on view-planning or MAPF. It does not resolve a long-standing scientific question, but it is a solid engineering contribution that deserves a careful referee rather than a desk reject. My recommendation: send it to review, but push the authors to report error bars, add the unconstrained baseline to the table, and qualify the outperform claim to match the actual scenario-level results.

Referee Report

5 major / 5 minor

Summary. The paper introduces CoCap, a coordinated multi-UAV view-planning approach for motion capture of multiple actors in cluttered outdoor environments. CoCap applies Conflict-Based Search (CBS) principles: a greedy joint plan is first computed without inter-robot constraints; when robot-robot conflicts are detected, constraints are added and the affected robots are replanned with a low-level single-agent view planner. The paper also presents a fast heuristic search variant for single-agent planning. Experiments in two simulated scenarios (corridor, bottleneck) report that CoCap with value iteration achieves higher total pixel-density reward than sequential planning (4662 vs 4127 in the corridor; 5162 vs 5053 in the bottleneck), while the search variant is much faster but scores lower (3922 in the corridor). The paper concludes that CoCap approaches the performance of unconstrained planning and outperforms sequential planning.

Significance. If the reported results are taken at face value, CoCap is a plausible approach for coordinating multiple camera-equipped robots in obstacle-rich environments, and the combination of CBS-style conflict resolution with a coverage objective is a reasonable extension of prior work. The single-agent search variant addresses real-time computation. The paper is honest in its limitations paragraph about cuboid actor models, 2.5D height maps, and absence of real-world deployment. However, the evidence is far from establishing the central claims: only two deterministic scenarios, no error bars, no reported unconstrained baseline totals, a reward surrogate that is not validated against actual pose reconstruction, and one variant underperforming the sequential baseline. These gaps mean the current version is not yet ready for archival publication.

major comments (5)

[Table I / Sec. VI-A] The unconstrained ("no inter-robot constraint") baseline reward is never reported numerically; Fig. 5 plots its cumulative reward but Table I omits it. Since the abstract claims that CoCap "approaches the ideal outcomes of unconstrained planning," the gap between CoCap's totals (4662, 5162) and the unconstrained totals must be quantified. Please report the unconstrained total reward for both scenarios and state the fraction of the gap recovered by CoCap.
[Table I / Fig. 5] All performance numbers in Table I and Fig. 5 come from a single deterministic run per scenario, with no repeated trials or variance measures. The corridor advantage is 4662 vs 4127, and the bottleneck advantage is only 5162 vs 5053; without error bars or multiple random initial positions, it is impossible to know whether these differences are robust. Please provide statistics over multiple runs (e.g., random robot starts or actor trajectories).
[Abstract / Table I, corridor row] CoCap (Search) achieves 3922 in the corridor, which is lower than Sequential (MDP) at 4127; the abstract nevertheless states without qualification that CoCap "outperforms existing sequential planning methods." Please either qualify the claim to "CoCap with value iteration" or explain why the search variant's lower reward is acceptable (e.g., it is a different trade-off point). Also, the bottleneck row lacks CoCap (Search) and all compute-time entries for the bottleneck scenario, making the comparison incomplete.
[Sec. VI / Eq. (1)] The evaluation uses the pixel-density coverage reward from GreedyPerspectives as the sole measure of performance. The paper's own limitations paragraph concedes that actors are modeled as cuboids, occlusion is via a 2.5D height map, and real multi-UAV deployment has not been tested. As a result, the abstract's claim about "motion capture" quality rests on an unvalidated surrogate. Please either provide evidence that the reward correlates with 4D pose reconstruction accuracy (e.g., running a pose estimator on rendered views) or restrict the claims to coverage reward.
[Algorithms 1 and 2 / Sec. V] The algorithms are under-specified and contain apparent inconsistencies. In Algorithm 2, line 8, the update "Rk+1 ← (Rk+1 + Rk)γk−1" references R_{k+1} before assignment, and the discounting is ambiguous; the branching factor ST, the discount factor γ, and the exact definition of availableActions (including obstacle and motion constraints) are not given. In Algorithm 1, the tree ordering is said to expand the node with max g, but CBS expands by least cost; the paper must state clearly that TREE is a max-priority queue and define tie-breaking. These details are needed to reproduce the reported results.

minor comments (5)

[Sec. I] There is a typo: "GreedyPrespectives" should be "GreedyPerspectives".
[Sec. III] The notation "g = J(Ξ)" is introduced without defining the function J or the symbol Ξ; please define these symbols in the problem formulation.
[Fig. 5] The curves in Fig. 5 are not labeled in the figure itself, and the captions read "Scale rewards" (likely "Scaled rewards"). Please add legends and explain the scaling.
[Sec. IV] The constraint ω_i = (i, u_i^a, u_i^b, t) should be explicitly defined as forbidding agent i from traversing the edge (u_i^a, u_i^b) at time t, rather than leaving it to the reader's inference.
[Sec. V-A] The expression "Px \ {Pxitk}" uses undefined subscripts i, t, k; please define this set-minus notation or replace it with a clearer description.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; the only self-citation is to the authors' prior reward/simulation setup and is not load-bearing for the coordination result.

full rationale

CoCap's central claim is an empirical comparison among coordinated, sequential, and unconstrained planners under the same pixel-density reward (Eq. 1). No fitted parameter is later renamed as a prediction, and no equation reduces to another by construction. The reward and simulation details are inherited from the authors' prior GreedyPerspectives work [7], as the paper states it is 'utilizing the reward structure employed by GreedyPerspectives [7]'; this is a minor self-citation, but both CoCap and its baselines are evaluated on that same reward, so the comparison is internally fair and not forced. The 'ideal outcomes of unconstrained planning' baseline is the unconstrained root node of CoCap's own constraint tree, so closing that gap is the algorithm's stated design objective, but the observed gap size is an empirical result rather than a mathematical identity. The limitations paragraph in Section VII raises external-validity concerns about the 2.5D height map, cuboid actor models, and the lack of real deployment, which are concerns about whether the pixel-density reward predicts real motion-capture quality, not circular dependencies. Thus the paper's coordination claim has independent content within the reported simulations.

Assumptions & free parameters 2 free parameters · 6 assumptions · 0 invented entities

The paper introduces no new physical entities. It relies on domain assumptions inherited from GreedyPerspectives and on CBS's conflict model. Free parameters are limited to search implementation choices such as the discount factor and branching factor.

free parameters (2)

Discount factor gamma = not specified
Algorithm 2 line 8 multiplies cumulative reward by gamma^(k-1) with gamma < 1 to favor shorter paths; the value is not reported and affects the single-agent search reward.
Action-space branching factor ST = not specified
Algorithm 2 line 6 samples ST available actions each timestep; this implementation choice changes the search tree size and solution quality but is not reported.

assumptions (6)

domain assumption Pixel-density coverage (Eq. 1) is a valid proxy for motion-capture reconstruction quality
The objective is taken from GreedyPerspectives [7]; the paper does not validate it against pose-reconstruction metrics.
domain assumption The coverage reward is submodular, justifying greedy and CBS-style node ordering
Section III says 'We employ submodular optimization techniques' but no proof or citation is given for this specific reward.
domain assumption Robot motion is discretized to a finite graph with actions that can be executed by real drones
Section III defines paths on graph G=(V,E); no feasibility or tracking error model is tested.
domain assumption A 2.5D height map is sufficient for occlusion and obstacle reasoning
Section VII limitation: overhangs, doors, and windows are not handled, yet the simulations rely on this map for coverage.
domain assumption Actors can be treated as cuboids with faces for coverage planning
Section VII limitation: actors are oversimplified, but the reward and experiments depend on face-based pixel coverage.
domain assumption Conflict-free vertex trajectories at discrete timesteps guarantee collision-free multi-UAV operation
The CBS conflict definition in Section IV is positional; continuous-space collision and dynamic feasibility are not modeled.

how reviews work

0 comments

Cite this review

Pith. "Pith review of CoCap: Coordinated motion Capture for multi-actor scenes in outdoor environments." pith.science (2026). https://pith.science/paper/RGZTNLN3

@misc{pith2026241220695,
  author       = {Pith},
  title        = {Pith review of: CoCap: Coordinated motion Capture for multi-actor scenes in outdoor environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RGZTNLN3}},
  note         = {Machine review of arXiv:2412.20695}
}

read the original abstract

Motion capture has become increasingly important, not only in computer animation but also in emerging fields like the virtual reality, bioinformatics, and humanoid training. Capturing outdoor environments offers extended horizon scenes but introduces challenges with occlusions and obstacles. Recent approaches using multi-drone systems to capture multiple actor scenes often fail to account for multi-view consistency and reasoning across cameras in cluttered environments. Coordinated motion Capture (CoCap), inspired by Conflict-Based Search (CBS), addresses this issue by coordinating view planning to ensure multi-view reasoning during conflicts. In scenarios with high occlusions and obstacles, where the likelihood of inter-robot collisions increases, CoCap demonstrates performance that approaches the ideal outcomes of unconstrained planning, outperforming existing sequential planning methods. Additionally, CoCap offers a single-robot view search approach for real-time applications in dense environments.

Figures

Figures reproduced from arXiv: 2412.20695 by the authors.

**Figure 1.** Coordinated View Planning: Coverage optimization on dynamic actors with flying cameras in an occlusion-aware and obstacle-clustered environment where camera extrinsic positions across robots are negotiated. filled with obstacles and occlusions, especially when cameras are maneuvered to obtain optimal coverage. This requires the drone system to not only manage obstacles and occlusions but also optimize for maximum co… view at source ↗

**Figure 2.** Sequential (Greedy) View Planning: On the left, there is the sequential view planning of multiple camera positions, where there are egocentric behaviors across multiple viewpoints as seen in the three camera outputs on the left under greedy planning. Coordinated planning, on right: we propose a coordinated view planning approach where there is pixel-level negotiation amongst view positions to allow non-egocentric be… view at source ↗

**Figure 3.** Problem representation of the gimbaled camera (also [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: Scale rewards using multiple cameras performing [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages

[1]

Multiple moving targets surveillance based on a cooperative network for multi-uav,

J. Gu, T. Su, Q. Wang, X. Du, and M. Guizani, “Multiple moving targets surveillance based on a cooperative network for multi-uav,”IEEE Communications Magazine, vol. 56, no. 4, pp. 82–89, 2018

work page 2018
[2]

Lsar: Multi- uav collaboration for search and rescue missions,

E. T. Alotaibi, S. S. Alqefari, and A. Koubaa, “Lsar: Multi- uav collaboration for search and rescue missions,” IEEE Access, vol. 7, pp. 55 817–55 832, 2019

work page 2019
[3]

Ocean front detection and tracking using a team of heterogeneous marine vehicles,

S. McCammon, G. Marcon dos Santos, M. Frantz, T. P. Welch, G. Best, R. K. Shearman, J. D. Nash, J. A. Barth, J. A. Adams, and G. A. Hollinger, “Ocean front detection and tracking using a team of heterogeneous marine vehicles,” Journal of Field Robotics, vol. 38, no. 6, pp. 854–881, 2021. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002...

work page doi:10.1002/rob.22014 2021
[4]

Integration of uav-based photogrammetry and terrestrial laser scanning for the three- dimensional mapping and monitoring of open-pit mine areas,

X. Tong, X. Liu, P. Chen, S. Liu, K. Luan, L. Li, S. Liu, X. Liu, H. Xie, Y . Jin, and Z. Hong, “Integration of uav-based photogrammetry and terrestrial laser scanning for the three- dimensional mapping and monitoring of open-pit mine areas,” Remote Sensing, vol. 7, no. 6, pp. 6635–6662, 2015. [Online]. Available: https://www.mdpi.com/2072-4292/7/6/6635

work page 2015
[5]

Informed sampling exploration path planner for 3d reconstruc- tion of large scenes,

Y . Kompis, L. Bartolomei, R. Mascaro, L. Teixeira, and M. Chli, “Informed sampling exploration path planner for 3d reconstruc- tion of large scenes,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 7893–7900, 2021

work page 2021
[6]

3D Human Reconstruction in the Wild with Collaborative Aerial Cameras

C. Ho, A. Jong, H. Freeman, R. Rao, R. Bonatti, and S. Scherer, “3d human reconstruction in the wild with collaborative aerial cameras,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Sep. 2021, pp. 5263–5269. [Online]. Available: https://arxiv.org/pdf/2108.03936

work page Pith review arXiv 2021
[7]

Greedy per- spectives: Multi-drone view planning for collaborative percep- tion in cluttered environments,

K. Suresh, A. Rauniyar, M. Corah, and S. Scherer, “Greedy per- spectives: Multi-drone view planning for collaborative percep- tion in cluttered environments,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2024

work page 2024
[8]

Shape-aware human pose and shape reconstruction using multi-view images,

J. Liang and M. C. Lin, “Shape-aware human pose and shape reconstruction using multi-view images,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 4352–4362

work page 2019

Show all 13 references

[9]

Fast and robust multi-person 3d pose estimation from multiple views,

J. Dong, W. Jiang, Q. Huang, H. Bao, and X. Zhou, “Fast and robust multi-person 3d pose estimation from multiple views,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 7792–7801

2019
[10]

Deep learning-based human pose estimation: A survey,

C. Zheng, W. Wu, C. Chen, T. Yang, S. Zhu, J. Shen, N. Ke- htarnavaz, and M. Shah, “Deep learning-based human pose estimation: A survey,” ACM Computing Surveys, vol. 56, no. 1, pp. 1–37, 2023

2023
[11]

Conflict-based search for optimal multi-agent pathfinding,

G. Sharon, R. Stern, A. Felner, and N. R. Sturtevant, “Conflict-based search for optimal multi-agent pathfinding,” Artificial Intelligence , vol. 219, pp. 40–66, 2015. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S0004370214001386

2015
[12]

Aircaprl: Autonomous aerial hu- man motion capture using deep reinforcement learning,

R. Tallamraju, N. Saini, E. Bonetto, M. Pabst, Y . T. Liu, M. Black, and A. Ahmad, “Aircaprl: Autonomous aerial hu- man motion capture using deep reinforcement learning,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 6678– 6685, October 2020, also accepted and pres...

2020
[13]

Multi- robot planning for filming groups of moving actors leveraging submodularity and pixel density,

S. Hughes, R. Martin, M. Corah, and S. Scherer, “Multi- robot planning for filming groups of moving actors leveraging submodularity and pixel density,” in Proceedings of the IEEE Conference on Decision and Control (CDC) , Milan, Italy, Dec. 2024, to appear

2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Multiple moving targets surveillance based on a cooperative network for multi-uav,

J. Gu, T. Su, Q. Wang, X. Du, and M. Guizani, “Multiple moving targets surveillance based on a cooperative network for multi-uav,”IEEE Communications Magazine, vol. 56, no. 4, pp. 82–89, 2018

work page 2018

[2] [2]

Lsar: Multi- uav collaboration for search and rescue missions,

E. T. Alotaibi, S. S. Alqefari, and A. Koubaa, “Lsar: Multi- uav collaboration for search and rescue missions,” IEEE Access, vol. 7, pp. 55 817–55 832, 2019

work page 2019

[3] [3]

Ocean front detection and tracking using a team of heterogeneous marine vehicles,

S. McCammon, G. Marcon dos Santos, M. Frantz, T. P. Welch, G. Best, R. K. Shearman, J. D. Nash, J. A. Barth, J. A. Adams, and G. A. Hollinger, “Ocean front detection and tracking using a team of heterogeneous marine vehicles,” Journal of Field Robotics, vol. 38, no. 6, pp. 854–881, 2021. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002...

work page doi:10.1002/rob.22014 2021

[4] [4]

Integration of uav-based photogrammetry and terrestrial laser scanning for the three- dimensional mapping and monitoring of open-pit mine areas,

X. Tong, X. Liu, P. Chen, S. Liu, K. Luan, L. Li, S. Liu, X. Liu, H. Xie, Y . Jin, and Z. Hong, “Integration of uav-based photogrammetry and terrestrial laser scanning for the three- dimensional mapping and monitoring of open-pit mine areas,” Remote Sensing, vol. 7, no. 6, pp. 6635–6662, 2015. [Online]. Available: https://www.mdpi.com/2072-4292/7/6/6635

work page 2015

[5] [5]

Informed sampling exploration path planner for 3d reconstruc- tion of large scenes,

Y . Kompis, L. Bartolomei, R. Mascaro, L. Teixeira, and M. Chli, “Informed sampling exploration path planner for 3d reconstruc- tion of large scenes,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 7893–7900, 2021

work page 2021

[6] [6]

3D Human Reconstruction in the Wild with Collaborative Aerial Cameras

C. Ho, A. Jong, H. Freeman, R. Rao, R. Bonatti, and S. Scherer, “3d human reconstruction in the wild with collaborative aerial cameras,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Sep. 2021, pp. 5263–5269. [Online]. Available: https://arxiv.org/pdf/2108.03936

work page Pith review arXiv 2021

[7] [7]

Greedy per- spectives: Multi-drone view planning for collaborative percep- tion in cluttered environments,

K. Suresh, A. Rauniyar, M. Corah, and S. Scherer, “Greedy per- spectives: Multi-drone view planning for collaborative percep- tion in cluttered environments,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2024

work page 2024

[8] [8]

Shape-aware human pose and shape reconstruction using multi-view images,

J. Liang and M. C. Lin, “Shape-aware human pose and shape reconstruction using multi-view images,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 4352–4362

work page 2019