A Low-Cost, Flexible and Portable Volumetric Capturing System

Alexandros Doumanoglou; Alexandros Papachristou; Antonis Karakottas; Dimitrios Zarpalas; Nikolaos Zioulis; Petros Daras; Vladimiros Sterzentsenko

REVIEW 3 major objections 5 minor 35 references

A Low-Cost, Flexible and Portable Volumetric Capturing System

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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

Pith's one-line read Four cardboard boxes replace checkerboards for 3D capture calibration

desk verdict A useful, publicly released capture system with a plausible markerless calibration method, but the evaluation's self-consistency metric cannot see a common geometric bias in the physical calibration structure. read the letter →

arxiv 1909.01207 v1 pith:VYPKPS5T submitted 2019-09-03 cs.CV

Vladimiros Sterzentsenko , Antonis Karakottas , Alexandros Papachristou , Nikolaos Zioulis , Alexandros Doumanoglou , Dimitrios Zarpalas , Petros Daras This is my paper · ORCID

classification cs.CV

keywords RGB-Dcamerasexternalcalibrationmulti-viewcapturevolumetricvideomarker-lessstructure-basedCNNsemanticsegmentationpointcloudregistration

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 is trying to establish that a complete volumetric capture system can be built from low-cost, off-the-shelf components and calibrated by a non-expert without waved checkerboards, markers, or optical trackers. The core claim is that a marker-less calibration procedure, based on a cardboard-box structure whose geometry is known exactly from a virtual 3D model, gives inter-view alignment errors of roughly 15 to 20 mm across a range of sensor placements. This matters because existing capture domes and production rigs are expensive, hard to relocate, and technically demanding to set up, which blocks wider use of volumetric video. If the claim holds, affordable 3D content creation for VR and AR becomes much more accessible.

What carries the argument

The central mechanism is the exact pairing of a physical calibration object with its virtual counterpart: four commercially available packaging boxes assembled into an asymmetric shape with 24 distinct sides, matching a 3D model that supplies both the rendered training data and the correspondence coordinates. The processing chain is: cylindrical pose sampling around the model generates noisy synthetic depth and normal maps; a multi-view CNN with per-branch encoder and decoder fuses $N$ randomly ordered depth inputs and jointly predicts side-label probabilities and normal maps under cross-entropy and $L_2$ losses; a fully connected CRF with Gaussian pairwise potentials over image positions and predicted normals refines the labels; and median back-projected points per label plus Procrustes analysis give each view an initial pose, with graph-optimized point-to-plane ICP producing the global solution. This chain carries the claim because the virtual model guarantees that every predicted label has known 3D coordinates.

What would settle it

Measure the real box dimensions of an assembled structure against the virtual model, then calibrate the same rig while deliberately deforming or slightly misaligning the boxes and compare the resulting poses to ground truth from a high-accuracy external tracker, such as a checkerboard with known world coordinates or a precision 3D scanner. If small dimensional deviations shift the calibration error by more than the claimed 15 to 20 mm, the exact-match assumption is the limiting factor; if the method stays within range, the assumption is safe.

Watch

Extended reading notes

Core claim

The paper's central discovery is that external calibration of an $N=4$ RGB-D capture system can be made robust and near-automatic by replacing checkerboards and markers with a fully asymmetric structure of four standardized packaging boxes. A virtual 3D model of the boxes is used to render synthetic depth maps with noise and random backgrounds, and a multi-view convolutional network with randomized input order learns to assign each visible box side one of 24 labels while also estimating surface normals; a dense CRF over labels and predicted normals cleans the segmentations. Median back-projected points from each labeled region provide 3D-to-3D correspondences solved by Procrustes analysis and refined by point-to-plane ICP in a graph optimization. On five different four-sensor placements, the method converges in every case with mean adjacent-view RMSE between $14.65$ and $19.83$ mm, while several marker- and ball-based baselines fail to converge, and the synthetic test set reaches $96.17\%$ mean intersection-over-union. The paper concludes that the method robustly produces high-quality external calibration with minimal human intervention and technical knowledge.

Load-bearing premise

The calibration's accuracy inherits from the virtual 3D model: if the real cardboard boxes are bent, misassembled, or dimensionally different from the standardized model, every estimated camera pose carries that error, and the paper does not quantify tolerance.

Editorial extensions

If this is right

A non-expert can recalibrate after moving the rig by assembling the same box structure, placing it in the capture volume, and running the provided pipeline; no checkerboard waving, QR markers, or per-experiment SIFT parameter tuning is required.
Because the calibration converges across the tested placement ranges (radii around 1.3 to 2.25 m and heights from 0.28 to 0.7 m), users have freedom to reconfigure a camera layout for a particular scene without redesigning the system.
The full system, including hardware choices, message-broker architecture, and calibration code, is released publicly, so comparable multi-view setups can be reproduced and extended rather than rebuilt from scratch.
For sensor arrangements not covered by the trained model, such as 3 sensors at 120-degree intervals or 8 sensors in two perimeters, the pipeline can generate a new synthetic training set and retrain, so the method is extensible beyond the demonstrated $N=4$ case.

Reading between the lines

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

If the box-geometry assumption holds in ordinary use, calibration becomes cheap and fast enough to run before every capture session, which would make mobile capture rigs practical: a rig could be broken down, transported, and reassembled on location without an expert.
The same virtual-model-to-CNN correspondence trick could transfer to other objects with known CAD geometry, such as product packaging or furniture, letting any rigid known-geometry object serve as a calibration target.
A direct sensitivity study, measuring the real assembled structure against the virtual model and correlating dimensional deviations with calibration error, would quantify how tightly the standardized-box assumption constrains accuracy; the paper leaves that tolerance unmeasured.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The paper you'd want to know about: it packages a practical volumetric capture rig (four RealSense D415s on tripods with NUCs) and a markerless external calibration method into a publicly released GitHub project. The calibration itself extends the authors' prior work [25] with an asymmetric non-planar box structure, a multi-view CNN, normal supervision, and a CRF refinement. That combination is reasonably novel, and the system-level contribution is real: a working, portable, low-cost rig that a non-expert could plausibly set up.

The headline result is the robustness story. Table I shows their method converging in all five sensor placements while several baselines fail outright. That is a genuine practical advantage, and the qualitative results look consistent. The architecture and training procedure are described clearly enough to reproduce, and the code availability helps.

The soft spots are mostly in the evaluation, and one of them is load-bearing. The exact-match assumption between the physically assembled packaging boxes and the virtual 3D model is never quantified. The stress-test note is on target: the reported metric—mean RMSE between closest points of adjacent views—is a self-consistency measure. It cannot detect a common scale or shear error if all four viewpoints observe the same slightly-off structure. So the 15–20 mm numbers do not establish that the physical structure actually matches the model. That hole is addressable with a controlled perturbation test (change box dimensions, re-run calibration, see how the poses change), but the paper doesn't do it.

Other weaknesses are real but proportional. There are no error bars or repeated calibration runs; the CNN mIoU is reported only on a synthetic test set; there is no direct comparison to the prior markerless method [25], so we can't see what the new structure and multi-view fusion actually buy; and the "low-cost" claim is never itemized. None of these sink the paper, but they prevent me from treating the calibration accuracy claim as fully established. The citation pattern is fine; the paper builds on relevant prior work and the authors' own [25] is a reasonable base.

Who should read this: anyone building a multi-view RGB-D capture rig, especially in a small studio or academic lab, and researchers working on markerless calibration. It's a systems paper with a useful working artifact.

My recommendation: this deserves a serious referee. I'd send it to review and ask the authors to add a perturbation sensitivity analysis, repeated runs with variance, and a direct comparison against [25]. With those, the central claim would be solid.

Referee Report

3 major / 5 minor

Summary. The paper presents a four-sensor RGB-D volumetric capture system built from commodity hardware (Intel RealSense D415 sensors, Intel NUCs, Ethernet switch) and a markerless structure-based external calibration method. Calibration requires the user to assemble and place a structure of standardized cardboard boxes with an asymmetric, non-planar layout. A CNN trained on synthetic depth renders of a virtual replica of the structure predicts per-view semantic labels and normal maps; a CRF refines the labels; correspondences are extracted as median back-projected points per labeled side; Procrustes analysis provides initial poses; and point-to-plane ICP under graph-based optimization refines a global alignment. The method is evaluated on five physical sensor placements against two marker-based baselines and two ball-based baselines, with the proposed method the only one to converge in all five placements. The implementation is released publicly.

Significance. If the claims hold, the paper makes a valuable practical contribution: a low-cost, portable, publicly available multi-view capture system whose calibration pipeline lowers the expertise barrier to checkerboard-free external calibration. The convergence results across five different sensor placements and the comparison against four existing methods are concrete and useful, and the public code release is a notable asset for the community. The main weakness is that the evaluation protocol does not yet substantiate the headline robustness and quality claims: the metrics are single-run self-consistency measures, and the central geometric assumption about the calibration structure is not stress-tested. Targeted experiments can address this; the core method appears plausible and not circular.

major comments (3)

[Section IV (Structure, Training Data, Correspondences and Optimization)] The calibration pipeline assumes that the physically assembled packaging-box structure exactly matches the virtual 3D model used for CNN training, correspondence generation, Procrustes initialization, and ICP refinement. The paper does not quantify the tolerance to deviations in box dimensions, cardboard deformation, or assembly misalignment. Since the evaluation metric in Section V (Table I) is the inter-view closest-point RMSE, a common geometric bias - for example a consistent scale or shear error of the structure - can displace all camera poses in a correlated way while leaving adjacent-view distances small. A controlled perturbation experiment (e.g. varying box dimensions or deliberately misassembling the structure) or a comparison against ground-truth poses from an external tracker is needed to support the concluding claim of Section V that the method 'robustly produces high quality external calibration results.'
[Section V (Table I and evaluation protocol)] Each reported RMSE value in Table I is a single run with no error bars, repeated trials, or statistical characterization. The robustness claim is about arbitrary user placement, so five converged configurations are suggestive but not sufficient evidence. Please report repeated calibrations per placement (e.g. 5-10 runs per configuration) with means and standard deviations, and state whether all compared methods were run by the same operator under the same protocol. Additionally, the absence of any ground-truth pose comparison means the absolute 15-20 mm figures cannot be separated from a possibly biased consensus reached by all viewpoints.
[Section V (Synthetic evaluation of the CNN)] The reported 96.17% mIoU is measured on a synthetic test set only. Because the final calibration operates on real sensor depth maps, the domain transfer of the segmentation network is not directly measured. A small manually labeled set of real depth maps, or at least a report of per-view correspondence success/failure counts on the five real placements, would substantiate that the synthetic supervision transfers to the physical structure. This is relevant to the load-bearing claim that the extracted correspondences reliably initialize the pose optimization.

minor comments (5)

[Figure 4 caption] The phrase 'planar side can be sheen with green overlay' should be 'planar side can be seen with green overlay.'
[References] References [21] and [29] refer to the same paper (LiveScan3D) and should be consolidated to avoid duplicate entries.
[Equation (1)] The notation U(a,b,c) for the uniform distributions is nonstandard; please state explicitly which variables are continuous and which are discretized, and define the meaning of the step parameter c.
[Equation (5)] The CRF energy is written as a 'per pixel p' energy, but the sum over all pixels i and pairs (i,j) describes a global energy; clarify the notation so the unary and pairwise terms are indexed consistently over the whole image.
[Section III (Sensor discussion)] The claim that the D415 supports inter-sensor hardware synchronization is important for the system design; a citation to the sensor datasheet or SDK documentation would strengthen this statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: pose estimates are computed from real depth data plus a known 3D model via Procrustes and ICP, while the CNN provides only semantic labels, not poses.

full rationale

The derivation chain is self-contained rather than circular. The CNN is trained offline on rendered depth/label/normal pairs from a virtual 3D model of the box structure (Section IV, Training Data), but at calibration time it outputs dense semantic side labels for real depth maps. The actual poses are obtained by back-projecting depths, extracting median 3D correspondences per labeled region, solving Procrustes against the known model, and refining with point-to-plane ICP under graph optimization (Section IV, Correspondences and Optimization). The training labels are not the calibration output; the CNN never directly regresses sensor poses. The reported evaluation metric (mean RMSE between closest points of adjacent views, Table I) is computed from captured point clouds, not from CNN outputs or training labels, so it is independent of the synthetic supervision. The only load-bearing citation to prior work by the same group is [25] for the graph-based dense optimization step, but that is a standard ICP refinement component, and the paper explicitly evaluates marker-based correspondence methods enhanced with the same optimization step (Section V), so the central claim does not reduce to the cited work. The concern that the physical box structure must exactly match the virtual model is a real robustness and evaluation limitation, and the inter-view RMSE metric cannot detect a common geometric bias, but that is a validity threat rather than a circular derivation. No fitted parameter is renamed as a prediction, and no result is defined in terms of the quantity it claims to estimate. The paper's own caveat that the calibration relies on a learned specific placement (Section VI) is a scope limitation, not evidence of circularity.

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

The central claims rest on the correspondence between the physical box structure and its virtual model, on synthetic-to-real transfer of the trained CNN, and on optimization steps adopted from prior work. No free parameters are fitted to the RMSE results themselves; the listed parameters are design choices that affect the pipeline.

free parameters (5)

Average D415 intrinsic parameters = Mean of 9 factory D415 sensors, downscaled by 4
Used to generate synthetic training data; a simplified sensor model that may not match any actual sensor's intrinsics, contributing to domain gap.
Loss weight lambda = 0.1
Chosen by hand to balance semantic and normal losses in Eq. (2); no ablation is provided.
Calibration structure dimensions = Not specified (standardized packaging boxes)
The boxes' dimensions define the global coordinate system; the paper gives no numeric dimensions, only 'standardized'.
CRF kernel widths sigma_2D and sigma_3D = Not reported
Appearing in Eq. (6) but values are not stated, making the refinement step not fully reproducible.
Pose sampling distribution ranges = See Eq. (1): rho, z, phi, e ranges
These control the synthetic training distribution; they must roughly match real placements, but are not fitted to data.

assumptions (4)

domain assumption The physical calibration structure exactly matches the virtual 3D model used for training and correspondence extraction.
Entered in Section IV (Structure and Correspondences and Optimization). If boxes deform, the computed correspondences are systematically biased.
domain assumption Depth maps from the D415 sensor, after downscaling and noise augmentation, are representative enough that a CNN trained on synthetic renders generalizes to real captures.
Assumed in Section IV Training Data; no real-to-synthetic domain adaptation or quantitative transfer evaluation is presented.
domain assumption The graph-based dense optimization and ICP from [25] are correct and applicable without modification.
Section IV says 'more details can be found at [25]', relying on the authors' prior work for a critical optimization step.
standard math Procrustes analysis and point-to-plane ICP converge to the global optimum for reasonable initial estimates.
Used in Section IV Correspondence and Optimization; standard methods, but convergence is not proven in the paper.

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Low-Cost, Flexible and Portable Volumetric Capturing System." pith.science (2026). https://pith.science/paper/VYPKPS5T

@misc{pith2026190901207,
  author       = {Pith},
  title        = {Pith review of: A Low-Cost, Flexible and Portable Volumetric Capturing System},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VYPKPS5T}},
  note         = {Machine review of arXiv:1909.01207}
}

read the original abstract

Multi-view capture systems are complex systems to engineer. They require technical knowledge to install and intricate processes to setup related mainly to the sensors' spatial alignment (i.e. external calibration). However, with the ongoing developments in new production methods, we are now at a position where the production of high quality realistic 3D assets is possible even with commodity sensors. Nonetheless, the capturing systems developed with these methods are heavily intertwined with the methods themselves, relying on custom solutions and seldom - if not at all - publicly available. In light of this, we design, develop and publicly offer a multi-view capture system based on the latest RGB-D sensor technology. For our system, we develop a portable and easy-to-use external calibration method that greatly reduces the effort and knowledge required, as well as simplify the overall process.

Figures

Figures reproduced from arXiv: 1909.01207 by the authors.

**Figure 2.** All these aforementioned components comprise a single coherent, Volumetric Capture system. Hardware: The physical interpretation of our eye acqui- §§https://software.intel.com/en-us/realsense/d400 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Update of the calibration structure. In (a) the old calibration structure is presented, on which the planar side can be sheen with green overlay. In (b) where the updated calibration structure is presented, there is no longer any coplanar side. Each side segments of the calibration structure can be seen in (c) Training Data: Our goal is to use the structure’s prior knowledge to establish correspondences between each… view at source ↗

Figures from the paper (5 more)

**Figure 3.** Figure 3: 3D capture snapshot acquired from the Volumetric Capture application showcasing the calibrated output when capturing a human subject. Each viewpoint’s pose is also depicted via the camera frustum placements. IV. PRACTICAL CALIBRATION The cornerstone of multi-view syst…

**Figure 5.** Figure 5: Pose generation sampling parameters illustration. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 6.** Figure 6: Our CNN architecture comprises 4 input encoding branches, one for each view, a bottleneck, and 4 output [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 7.** Figure 7: shows the output of the CNN for a quadruple of depth maps for experiment b, and then presents the output of the post-refinement step that improves the quality of the labeled regions. This helps in establishing more accurate correspondences for the initial pose estimate…

**Figure 8.** Figure 8: Qualitative results of the obtained external cali [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

35 extracted references · 32 canonical work pages

[25]

Markerless structure-based multi-sensor calibration for free viewpoint video capture,

A. Papachristou, N. Zioulis, D. Zarpalas, and P. Daras, “Markerless structure-based multi-sensor calibration for free viewpoint video capture,” in Proceedings of 26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision’2018 , ser. WSCG ’18, 2018, pp. 88–97. [Online]. Available: http://wscg.zcu.cz/WSCG2018/!! ...

work page 2018
[1]

High- quality streamable free-viewpoint video,

A. Collet, M. Chuang, P. Sweeney, D. Gillett, D. Evseev, D. Calabrese, H. Hoppe, A. Kirk, and S. Sullivan, “High- quality streamable free-viewpoint video,” ACM Transac- tions on Graphics (TOG) , vol. 34, no. 4, p. 69, 2015

work page 2015
[2]

Holoportation: Virtual 3d tele- portation in real-time,

S. Orts-Escolano, C. Rhemann, S. Fanello, W. Chang, A. Kowdle, Y . Degtyarev, D. Kim, P. L. Davidson, S. Khamis, M. Dou et al. , “Holoportation: Virtual 3d tele- portation in real-time,” in Proceedings of the 29th Annual Symposium on User Interface Software and Technology . ACM, 2016, pp. 741–754

work page 2016
[3]

An integrated platform for live 3d human reconstruction and motion capturing,

D. S. Alexiadis, A. Chatzitoﬁs, N. Zioulis, O. Zoidi, G. Louizis, D. Zarpalas, and P. Daras, “An integrated platform for live 3d human reconstruction and motion capturing,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 4, pp. 798–813, April 2017

work page 2017
[4]

Multi-view performance capture of surface details,

N. Robertini, D. Casas, E. De Aguiar, and C. Theobalt, “Multi-view performance capture of surface details,” International Journal of Computer Vision , vol. 124, no. 1, pp. 96–113, Aug 2017. [Online]. Available: https://doi.org/10.1007/s11263-016-0979-1

work page doi:10.1007/s11263-016-0979-1 2017
[5]

Free-viewpoint video of human actors using multiple handheld kinects,

G. Ye, Y . Liu, Y . Deng, N. Hasler, X. Ji, Q. Dai, and C. Theobalt, “Free-viewpoint video of human actors using multiple handheld kinects,” IEEE Transactions on Cyber- netics, vol. 43, no. 5, pp. 1370–1382, Oct 2013

work page 2013
[6]

Performance capture from sparse multi-view video,

E. De Aguiar, C. Stoll, C. Theobalt, N. Ahmed, H.-P. Seidel, and S. Thrun, “Performance capture from sparse multi-view video,” ACM Transactions on Graphics (TOG) , vol. 27, no. 3, p. 98, 2008

work page 2008
[7]

Motion capture using joint skeleton tracking and surface estimation,

J. Gall, C. Stoll, E. De Aguiar, C. Theobalt, B. Rosenhahn, and H.-P. Seidel, “Motion capture using joint skeleton tracking and surface estimation,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009, pp. 1746–1753

work page 2009

Show all 35 references

[8]

Sweeping-based volumetric calibration and registration of multiple rgbd-sensors for 3d capturing systems,

S. Beck and B. Froehlich, “Sweeping-based volumetric calibration and registration of multiple rgbd-sensors for 3d capturing systems,” in Virtual Reality (VR), 2017 IEEE . IEEE, 2017, pp. 167–176

2017
[9]

Multiview imaging and 3dtv,

A. Kubota, A. Smolic, M. Magnor, M. Tanimoto, T. Chen, and C. Zhang, “Multiview imaging and 3dtv,” IEEE signal processing magazine, vol. 24, no. 6, pp. 10–21, 2007

2007
[10]

Virtualized reality: Constructing virtual worlds from real scenes,

T. Kanade, P. Rander, and P. Narayanan, “Virtualized reality: Constructing virtual worlds from real scenes,” IEEE multimedia, vol. 4, no. 1, pp. 34–47, 1997

1997
[11]

An immersive 3d video- conferencing system using shared virtual team user environ- ments,

P. Kauff and O. Schreer, “An immersive 3d video- conferencing system using shared virtual team user environ- ments,” in Proceedings of the 4th international conference on Collaborative virtual environments . ACM, 2002, pp. 105–112

2002
[12]

Panoptic studio: A massively multiview system for social interac- tion capture,

H. Joo, T. Simon, X. Li, H. Liu, L. Tan, L. Gui, S. Banerjee, T. S. Godisart, B. Nabbe, I. Matthews et al. , “Panoptic studio: A massively multiview system for social interac- tion capture,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017

2017
[13]

A real-time interactive multi- view video system,

J.-G. Lou, H. Cai, and J. Li, “A real-time interactive multi- view video system,” in Proceedings of the 13th annual ACM international conference on Multimedia . ACM, 2005, pp. 161–170

2005
[14]

A real-time coarse-to-ﬁne multiview capture system for all-in-focus rendering on a light-ﬁeld display,

F. Marton, E. Gobbetti, F. Bettio, J. A. I. Guiti ´an, and R. Pintus, “A real-time coarse-to-ﬁne multiview capture system for all-in-focus rendering on a light-ﬁeld display,” in 3DTV Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON), 2011 . I...

2011
[15]

A versatile camera calibration technique for high- accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses,

R. Tsai, “A versatile camera calibration technique for high- accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses,” IEEE Journal on Robotics and Automation, vol. 3, no. 4, pp. 323–344, 1987

1987
[16]

Design and calibration of a multi-view tof sensor fusion system,

Y . M. Kim, D. Chan, C. Theobalt, and S. Thrun, “Design and calibration of a multi-view tof sensor fusion system,” in Computer Vision and Pattern Recognition Workshops,
[17]

Using multiple rgb-d cameras for 3d video acquisition and spatio-temporally coherent 3d an- imation reconstruction,

N. Ahmed and I. Junejo, “Using multiple rgb-d cameras for 3d video acquisition and spatio-temporally coherent 3d an- imation reconstruction,” International Journal of Computer Theory and Engineering , vol. 6, no. 6, p. 447, 2014

2014
[18]

Markerless motion capture using multiple color-depth sensors

K. Berger, K. Ruhl, Y . Schroeder, C. Bruemmer, A. Scholz, and M. A. Magnor, “Markerless motion capture using multiple color-depth sensors.” in VMV, 2011, pp. 317–324

2011
[19]

Omnikinect: Real- time dense volumetric data acquisition and applications,

B. Kainz, S. Hauswiesner, G. Reitmayr, M. Steinberger, R. Grasset, L. Gruber, E. Veas, D. Kalkofen, H. Seichter, and D. Schmalstieg, “Omnikinect: Real- time dense volumetric data acquisition and applications,” in Proceedings of the 18th ACM Symposium on Virtual Reality Softwar...

2012
[20]

3d tele- immersion platform for interactive immersive experiences between remote users,

N. Zioulis, D. Alexiadis, A. Doumanoglou, G. Louizis, K. Apostolakis, D. Zarpalas, and P. Daras, “3d tele- immersion platform for interactive immersive experiences between remote users,” in 2016 IEEE International Confer- ence on Image Processing (ICIP) , Sept 2016, pp. 365–369

2016
[21]

Live scan3d: A fast and inexpensive 3d data acquisition system for multiple kinect v2 sensors,

M. Kowalski, J. Naruniec, and M. Daniluk, “Live scan3d: A fast and inexpensive 3d data acquisition system for multiple kinect v2 sensors,” in 3D Vision (3DV), 2015 International Conference on. IEEE, 2015, pp. 318–325

2015
[22]

A ﬂexible new technique for camera calibra- tion,

Z. Zhang, “A ﬂexible new technique for camera calibra- tion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330–1334, Nov 2000

2000
[23]

Automatic graph based spatiotemporal extrinsic calibration of multiple kinect v2 tof cameras,

A. Fornaser, P. Tomasin, M. De Cecco, M. Tavernini, and M. Zanetti, “Automatic graph based spatiotemporal extrinsic calibration of multiple kinect v2 tof cameras,” Robotics and Autonomous Systems , vol. 98, pp. 105–125, 2017

2017
[24]

A fast and robust extrinsic calibration for rgb-d camera networks,

P.-C. Su, J. Shen, W. Xu, S.-C. S. Cheung, and Y . Luo, “A fast and robust extrinsic calibration for rgb-d camera networks,” Sensors, vol. 18, no. 1, p. 235, 2018

2018
[26]

Intel realsense stereo- scopic depth cameras,

L. Keselman, J. Iselin Woodﬁll, A. Grunnet-Jepsen, A. Bhowmik, M. Gupta, A. Jauhari, K. Kulkarni, S. Jaya- suriya, A. Molnar, P. Turaga et al. , “Intel realsense stereo- scopic depth cameras,” in The IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR) Workshops
[27]

TurboJPEG,

“TurboJPEG,” https://github.com/libjpeg-turbo/libjpeg- turbo, accessed: 2018-09-03

2018
[28]

“Blosc,” https://github.com/Blosc/c-blosc, accessed: 2018- 09-03

2018
[29]

Livescan3d: A fast and inexpensive 3d data acquisition system for multiple kinect v2 sensors,

M. Kowalski, J. Naruniec, and M. Daniluk, “Livescan3d: A fast and inexpensive 3d data acquisition system for multiple kinect v2 sensors,” in 2015 International Conference on 3D Vision, Oct 2015, pp. 318–325

2015
[30]

Posenet: A convo- lutional network for real-time 6-dof camera relocalization,

A. Kendall, M. Grimes, and R. Cipolla, “Posenet: A convo- lutional network for real-time 6-dof camera relocalization,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2938–2946

2015
[31]

Efﬁcient inference in fully connected crfs with gaussian edge potentials,

P. Kr ¨ahenb¨uhl and V . Koltun, “Efﬁcient inference in fully connected crfs with gaussian edge potentials,” in Advances in neural information processing systems , 2011, pp. 109– 117

2011
[32]

A survey of the statistical theory of shape,

D. G. Kendall, “A survey of the statistical theory of shape,” Statistical Science , pp. 87–99, 1989

1989
[33]

Caffe: Con- volutional architecture for fast feature embedding,

Y . Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Con- volutional architecture for fast feature embedding,” arXiv preprint arXiv:1408.5093, 2014

2014 arXiv
[34]

Adam: A method for stochastic optimization,

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

2014 arXiv
[2008]

IEEE Computer Society Conference on

CVPRW’08. IEEE Computer Society Conference on . IEEE, 2008, pp. 1–7

2008

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [25]

Markerless structure-based multi-sensor calibration for free viewpoint video capture,

A. Papachristou, N. Zioulis, D. Zarpalas, and P. Daras, “Markerless structure-based multi-sensor calibration for free viewpoint video capture,” in Proceedings of 26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision’2018 , ser. WSCG ’18, 2018, pp. 88–97. [Online]. Available: http://wscg.zcu.cz/WSCG2018/!! ...

work page 2018

[2] [1]

High- quality streamable free-viewpoint video,

A. Collet, M. Chuang, P. Sweeney, D. Gillett, D. Evseev, D. Calabrese, H. Hoppe, A. Kirk, and S. Sullivan, “High- quality streamable free-viewpoint video,” ACM Transac- tions on Graphics (TOG) , vol. 34, no. 4, p. 69, 2015

work page 2015

[3] [2]

Holoportation: Virtual 3d tele- portation in real-time,

S. Orts-Escolano, C. Rhemann, S. Fanello, W. Chang, A. Kowdle, Y . Degtyarev, D. Kim, P. L. Davidson, S. Khamis, M. Dou et al. , “Holoportation: Virtual 3d tele- portation in real-time,” in Proceedings of the 29th Annual Symposium on User Interface Software and Technology . ACM, 2016, pp. 741–754

work page 2016

[4] [3]

An integrated platform for live 3d human reconstruction and motion capturing,

D. S. Alexiadis, A. Chatzitoﬁs, N. Zioulis, O. Zoidi, G. Louizis, D. Zarpalas, and P. Daras, “An integrated platform for live 3d human reconstruction and motion capturing,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 4, pp. 798–813, April 2017

work page 2017

[5] [4]

Multi-view performance capture of surface details,

N. Robertini, D. Casas, E. De Aguiar, and C. Theobalt, “Multi-view performance capture of surface details,” International Journal of Computer Vision , vol. 124, no. 1, pp. 96–113, Aug 2017. [Online]. Available: https://doi.org/10.1007/s11263-016-0979-1

work page doi:10.1007/s11263-016-0979-1 2017

[6] [5]

Free-viewpoint video of human actors using multiple handheld kinects,

G. Ye, Y . Liu, Y . Deng, N. Hasler, X. Ji, Q. Dai, and C. Theobalt, “Free-viewpoint video of human actors using multiple handheld kinects,” IEEE Transactions on Cyber- netics, vol. 43, no. 5, pp. 1370–1382, Oct 2013

work page 2013

[7] [6]

Performance capture from sparse multi-view video,

E. De Aguiar, C. Stoll, C. Theobalt, N. Ahmed, H.-P. Seidel, and S. Thrun, “Performance capture from sparse multi-view video,” ACM Transactions on Graphics (TOG) , vol. 27, no. 3, p. 98, 2008

work page 2008

[8] [7]

Motion capture using joint skeleton tracking and surface estimation,

J. Gall, C. Stoll, E. De Aguiar, C. Theobalt, B. Rosenhahn, and H.-P. Seidel, “Motion capture using joint skeleton tracking and surface estimation,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009, pp. 1746–1753

work page 2009