The quantity at the heart of the study is electroluminescent quantum efficiency, a value commonly used to determine how well a solar cell converts light into electricity. When a solar cell is electrically biased, it emits a faint glow in the infrared, and the intensity of that luminescence is directly related to the cell’s voltage. Generally speaking, the higher the luminescent quantum efficiency, the better the solar cell, because a more efficient device radiates more light for a given electrical input. Measuring this value has traditionally required dedicated scientific cameras whose response to light must be precisely characterized, a process that adds significant cost and complexity to quality control in photovoltaics manufacturing and research.

Our approach shows that even a relatively inexpensive consumer camera can provide quantitative results when its physical response is properly modeled and calibrated, said author Jürgen Werner. The key insight of the team is that the camera does not need to be a purpose-built scientific detector as long as the way it responds to incoming photons is fully understood. By constructing a suitable physical camera model, the researchers could convert raw pixel brightness into absolute luminescent quantum efficiency, turning an ordinary photograph into a map of local solar cell quality.

The camera the team selected was already primed for the experiment because it lacked an internal infrared-blocking filter, a component that is normally present in most commercial cameras. Manufacturers install these filters so that consumer photographs look natural to the human eye, but their removal opens a window into the near-infrared, where solar cells emit their electroluminescence. Cameras with this filter removed are also popular for artistic infrared photography and for capturing images of the night sky, which means the hardware required for the measurement is accessible to hobbyists as well as laboratories.

To suppress unwanted visible light, the researchers added a long-pass filter in front of the camera lens. This optical component blocks shorter wavelengths, including the visible background light that would otherwise contaminate the signal, while allowing the longer infrared wavelengths of the electroluminescence to pass through to the sensor. The result is a clean infrared electroluminescence image that can later be analyzed based on brightness, with the pixel values encoding quantitative information about the emitting device rather than merely a pretty picture.

An electroluminescence image contains much more quantitative information than simply showing bright and dark regions, said Werner. With a suitable physical camera model and calibration, it can provide absolute luminescent quantum efficiency and, therefore, information about the local quality of a solar cell or module. This local dimension is particularly important in practical photovoltaics, where defects, cracks, or degraded regions within a single cell or module can drag down overall performance even when the rest of the device is healthy. A calibrated consumer camera can therefore reveal exactly where a solar module is losing efficiency, something that previously required laboratory-grade imaging systems.

The implications for the solar industry could be substantial. Quality assurance in photovoltaics manufacturing relies on electroluminescence imaging to spot defects on production lines, and field inspections of installed modules use the same technique to identify degradation caused by weathering, microcracks, or faulty connections. If the physical model developed by the Stuttgart and Jülich teams proves robust across different consumer cameras, the barrier to performing absolute quantum efficiency measurements could drop dramatically, allowing smaller companies, research groups in developing institutions, and even advanced inspection services to carry out measurements that were once the exclusive domain of well-funded laboratories.

The methodology also carries broader lessons for experimental physics. The study demonstrates that the limiting factor in many optical measurements is not the detector itself but the degree to which its response is understood. A consumer camera sensor, with its Bayer color filter array, its nonlinear response curve, and its manufacturer-specific processing, is a far messier detector than a scientific CCD, yet the researchers showed that all of these quirks can be folded into a physical model. Once calibrated, the messy detector becomes a quantitative one. This modeling philosophy could extend well beyond photovoltaics, wherever faint light emission carries information about the underlying device.

The researchers plan to expand the camera’s capabilities in future work, keeping an emphasis on using commercially available technology. Our next step is to use the calibrated camera to determine quantum efficiencies and open-circuit voltages of further, previously uncharacterized solar cells and modules, said Werner. The same model should also be applicable to photoluminescence measurements and potentially to measurements performed in daylight. Photoluminescence imaging, in which sunlight or a laser excites the solar cell and the emitted light is analyzed, is another cornerstone technique in photovoltaics characterization, and daylight operation would be transformative for field inspections, where modules currently must often be measured in darkness or under controlled conditions.

The article New Camera Model for Absolute Quantum Efficiency Measurements from Electroluminescence of Solar Cells or Modules is authored by Jürgen H. Werner, Georgette Udo, and Liviu Stoicescu, and it appears in The Journal of Applied Physics on Sept. 22, 2026. As solar energy continues its rapid global expansion, the demand for fast, affordable, and accurate efficiency measurements grows with it, and this study suggests that the tools required may already be sitting in camera shops around the world. With a removed infrared filter, a long-pass filter, and a well-built physical model, an ordinary camera becomes a window into the invisible light that reveals exactly how good a solar cell really is.

Subject of Research: Electroluminescence-based quantum efficiency measurement of solar cells using a calibrated consumer camera

Article Title: A normal camera that can detect solar cell efficiency

Article References: A normal camera that can detect solar cell efficiency. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: solar cells, electroluminescence, quantum efficiency, consumer camera, photovoltaics, infrared imaging, camera calibration, The Journal of Applied Physics, solar cell efficiency, long-pass filter, open-circuit voltage, photoluminescence

JANUS, whose full name Jovis, Amorum ac Natorum Undique Scrutator translates roughly as a scrutinizer of Jupiter, his loves and his children from all sides, is the workhorse imaging system on JUICE, the JUpiter ICy moons Explorer. The spacecraft launched in April 2023 on a mission that will ultimately deliver it to Jupiter in 2031, where it will spend years studying the gas giant and three of its large icy moons: Ganymede, Callisto and Europa. Before it can begin that science campaign, however, JUICE must execute a series of gravity assists, using the masses of Earth, the Moon and Venus to bend its trajectory and build up the enormous velocity needed to reach the outer solar system. The August 2024 Lunar–Earth flyby was the first of these crucial maneuvers, and mission planners recognized early on that it offered a rare opportunity to test the spacecraft’s instruments against a familiar target.

The choice of Earth as a calibration and validation target is more scientifically astute than it might first appear. Earth is, in effect, a rehearsal stage for planetary atmospheric imaging. Its cloud systems, storm dynamics, aerosol layers and day–night contrast phenomena operate under physical principles that also govern the atmospheres of the giant planets, albeit at different temperatures, compositions and rotation rates. By pointing JANUS at terrestrial weather systems, the science team could exercise the camera’s full observational chain, from pointing accuracy and exposure control to onboard data handling and image calibration, while simultaneously collecting genuine atmospheric data. The result is a two-for-one payoff: the instrument is validated for Jupiter, and researchers gain a benchmark dataset of Earth observations made with the very same hardware they will later use on the king of planets.

During the close portion of the flyby, JANUS acquired images of both the night side and the day side of Earth, a demanding test because the illumination conditions differ by many orders of magnitude. Day-side imaging exercises the camera’s ability to handle bright, sunlit scenes with fine spatial detail, revealing cloud structures, storm systems and surface features with the kind of contrast that will be essential for tracking Jovian weather. Night-side imaging, by contrast, pushes the sensitivity limits of the detector and tests how well the instrument can capture faint phenomena such as airglow, lightning-like flashes and the subtle glow of the upper atmosphere. Both regimes will matter enormously at Jupiter, where the planet’s rapid rotation, deep cloud decks and powerful auroral emissions create scenes of extreme dynamic range that few cameras in history have been asked to record.

The close flyby images allowed the team to characterize specific atmospheric systems in detail, and the analysis published in Annales Geophysicae focuses precisely on these features as relevant examples for future Jovian studies. Terrestrial cyclones, frontal systems and convective storm clusters observed by JANUS serve as controlled case studies: researchers know the ground truth of Earth’s weather from satellites, radar and surface observations, so they can assess how well JANUS resolves and measures such phenomena. When the same camera later images Jupiter’s anticyclones, its turbulent jet streams and its towering convective outbreaks, the team will be able to interpret the data with confidence grounded in this terrestrial validation. The methodology mirrors a long tradition in planetary science, in which flyby targets are used to cross-calibrate instruments against well-understood references before the main mission begins.

Beyond the close-ups, JUICE also captured a distant view of Earth that produced a striking low-resolution portrait of the entire planet. Such whole-disk images may seem less glamorous than detailed cloud mosaics, but they serve a distinct technical purpose. They test the camera’s performance at long range, verifying pointing, focus and radiometric calibration under conditions that resemble, in some respects, the approach phase at Jupiter and the observation of small moons from orbit. Whole-disk imaging also provides context for the higher-resolution work, and the Earth portrait stands as a poignant scientific artifact in its own right, a reminder of the pale blue dot that every interplanetary mission carries with it in memory as it ventures outward.

The validation exercise matters because JANUS faces one of the most challenging imaging assignments ever attempted at Jupiter. The planet’s atmosphere is a seething laboratory of fluid dynamics: alternating east–west jet streams reach hundreds of kilometers per hour, storms the size of Earth persist for decades or centuries, and lightning discharges far more energetic than any terrestrial flash illuminate the deep cloud layers. Understanding these phenomena requires high-resolution, time-resolved imaging that can track cloud motions and measure wind fields, and it demands a camera whose calibration is known with exquisite precision. Any systematic error in JANUS’s response, whether in geometric distortion, radiometric sensitivity or temporal stability, would propagate directly into errors in derived wind speeds and cloud heights. The Earth observations provide the empirical foundation for eliminating those errors before the spacecraft arrives at its destination.

There is also a deeper scientific continuity at play. Comparative planetology thrives on observing the same physical processes in different planetary settings, and Earth offers the most densely observed atmosphere in the solar system. Convective storms on Earth, driven by water condensation and latent heat release, have direct analogues in Jupiter’s water-cloud thunderstorms, which are thought to power the planet’s most vigorous eruptions. Cyclonic vortices on Earth can be compared with Jovian oval storms to test theories of vortex stability and decay. Even the night-side airglow observed by JANUS connects to upper-atmosphere physics that operates on Jupiter as well. By building a JANUS dataset of well-characterized terrestrial phenomena, the team has created a Rosetta stone that will help translate between what we know about Earth’s atmosphere and what we hope to learn about Jupiter’s.

The August 2024 flyby itself was a feat of celestial navigation. JUICE threaded a path around the Earth–Moon system, using the combined gravitational pull of both bodies to reshape its orbit, a maneuver type so delicate that only a handful of spacecraft have ever attempted it. Every instrument that observed during the encounter had to operate autonomously, with precise timing, because the geometry of a fast flyby leaves no room for second chances. That JANUS delivered calibrated, scientifically useful images of both the day and night sides of Earth on the first attempt speaks to the maturity of the instrument design and the thoroughness of the team led by Hueso, Antuñano, Lara and their international collaborators spanning Spain, Germany, Italy, France, Israel, Japan, the United Kingdom and the European Space Agency itself.

As JUICE continues its interplanetary cruise, with further gravity assists still ahead before its 2031 arrival at Jupiter, the JANUS Earth observations will remain a touchstone for the mission’s imaging science. They confirm that the camera is ready to tackle the gas giant’s atmosphere, and they have already begun producing science, offering fresh perspectives on our own planet from a vantage point few cameras have shared. When JANUS finally turns its gaze from Earth to Jupiter, every cloud deck it resolves and every storm it tracks will rest on the foundation laid during those August days in 2024, when a Jupiter camera came home briefly, looked at its makers’ world, and proved itself worthy of the giant that awaits.

Subject of Research: JANUS camera observations of Earth during the JUICE mission's 2024 Lunar–Earth gravity assist as validation for imaging Jupiter's atmosphere

Article Title: JUICE-JANUS observations of Earth in preparation for the JANUS investigation of Jupiter's atmosphere

Article References: Hueso, R., Antuñano, A., Lara, L. M., Stephan, K., Zinzi, A., Coustenis, A., Yair, Y., Sato, M., Haruyama, J., Simon, A., Tubiana, C., Penasa, L., Agostini, L., Lucchetti, A., Aboudan, A., Aye, M., Kersten, E., Matz, K.-D., Politi, R., … Palumbo, P. (2026). JUICE-JANUS observations of Earth in preparation for the JANUS investigation of Jupiter's atmosphere. Annales Geophysicae, 44(2), 1043-1073. https://doi.org/10.5194/angeo-44-1043-2026

Image Credits: AI Generated

DOI: 10.5194/angeo-44-1043-2026

Keywords: JUICE, JANUS, ESA, Jupiter, Earth flyby, gravity assist, planetary science, atmospheric science, space instrumentation, camera calibration, comparative planetology, Annales Geophysicae

The scale of the problem is easy to underestimate because the failure mode is nearly invisible. When a pinhole calibration is fitted to data collected through a refractive interface, the optimization does not crash or warn the user; instead it absorbs much of the mismatch into an inflated focal length and spurious lens distortion. Earlier studies by Sedlazeck and Koch, and by Jordt and colleagues, characterized this behavior for cameras mounted close behind underwater ports, finding that residual triangulation error grows with the camera’s standoff from the interface and with the distance to the imaged point, eventually reaching several percent of the camera-to-point distance. Jordt’s group observed depth errors of roughly 20 centimeters for surfaces at 2 meters. Those studies examined standoffs of centimeters; a fixed camera array imaging an entire aquarium tank from a meter or more away operates an order of magnitude beyond that range, where the errors grow in direct proportion to the standoff.

What makes the bias especially dangerous is that the checks practitioners usually run cannot detect it. Distances between nearby reconstructed points remain sub-millimeter under both a pinhole model and a corrected one, across a wide range of detection noise. Revealing the systematic depth-axis error requires an absolute-position check or a held-out rigid object spanning a long depth baseline, measurements that few pipelines routinely make. The affected fields are broad, spanning behavioral biology, aquaculture monitoring, underwater archaeology, and robotic navigation, and once the bias enters at calibration it silently contaminates everything reconstructed from the model.

Refraction-aware calibration theory is itself well established. Treibitz and colleagues showed that flat-interface refraction produces depth-dependent distortion that no radial polynomial can capture across the imaging volume, Agrawal and collaborators derived the general multi-layer flat-refraction theory, and Chadebecq’s team extended refractive geometry to two-view reconstruction. Recent systems such as Refractive COLMAP, the CalibMar toolbox, and a virtual-imaging method for binocular laser scanners build on these foundations. But all three estimate a separate interface distance for each camera, treating one physical plane as many. For a fixed array sharing a single water surface, that per-camera approach leaves each camera’s position along the interface normal redundant with its own interface height, an estimate that Treibitz’s work shows to be ill-conditioned unless the target reaches the edge of the frame, where refraction is strongest. No existing open-source tool was built to exploit the shared interface as geometric structure.

AquaCal, developed by Tucker Lancaster and Patrick McGrath at the Georgia Institute of Technology, fills that gap. It is a pip-installable Python library that models refraction at a single planar interface explicitly, applying Snell’s law in both projection directions, and folds the physics into one joint bundle adjustment over the camera extrinsics, a single global interface height, and the per-frame placement of the calibration target. The world frame is oriented with its Z axis along the interface normal, so the interface becomes the single horizontal plane at a height the software calls water_z. Each camera’s physical standoff, the gap that enters Snell’s law, is then derived directly from that one shared parameter. In synthetic tests, replacing the shared parameter with a per-camera estimate placed one physical surface at twelve different heights spanning 11 millimeters, and recovered every camera’s standoff less accurately across all ten random seeds tested.

The calibration runs in three stages, each designed around the conditions under which its parameters are best estimated. Stage 1 estimates lens intrinsics from in-air video of a small ChArUco board, a chessboard pattern embedded with ArUco fiducial markers, using OpenCV and supporting standard, rational, and fisheye lens models. This separation matters because under refraction a camera’s focal length and its standoff trade off against each other, making a joint fit from underwater observations alone poorly conditioned, whereas in air the projection is unambiguous. Stage 2 seeds the final joint solve, which is non-convex and converges reliably only from a starting point already close to the solution, by chaining pairwise relative camera positions and fusing the redundant links through rotation averaging, without requiring any single board placement visible to every camera at once. Stage 3 then estimates everything together, minimizing the refractive reprojection error across all observations, with an optional second pass that unlocks each camera’s focal length and principal point only after the geometry has converged.

The engineering beneath the pipeline is as carefully considered as the geometry. AquaCal computes the Jacobian of its optimization by finite differences over an explicit block-sparse pattern, applying Curtis–Powell–Reid column grouping so that the number of evaluations is a property of a single observation rather than of the array, meaning the savings grow as cameras and, more steeply, frames are added. A Huber robust loss down-weights outlier corner detections without a separate filtering stage. Cameras with high-distortion optics or limited target visibility can be designated auxiliary, registered after the fact against the frozen shared parameters so they cannot perturb the primary solution, and flagged in the output so downstream users can weigh their accuracy cost. The whole optimization scales to rigs of a dozen or more cameras in under twenty minutes on a workstation, with peak memory use near 11 gigabytes, and terminates at a cost tolerance of 1e-8 rather than an iteration cap.

The validation results are striking. In synthetic tests of an idealized twelve-camera rig about a meter above a tank, with 0.5 pixels of Gaussian noise added, the refractive model held focal lengths near ground truth with a mean absolute drift of just 0.066 percent, while the non-refractive baseline drifted by 6.83 percent, consistently overestimating focal length to compensate for the apparent-depth compression that refraction introduces. The refractive model drove reprojection error to the noise floor at 0.498 pixels, while the baseline plateaued about 2.5 times higher. The gap widened dramatically in 3D reconstruction. Triangulating held-out board placements at depths from 0.07 to 1.47 meters below the surface, the refractive model’s depth-axis error stayed flat between 1.7 and 1.9 millimeters across the entire tested volume, while the baseline climbed from 17.6 millimeters to 243 millimeters at the farthest point, roughly 2.5 meters from the reference camera, an approximately 132-fold degradation. Local scale accuracy told a complementary story: the refractive model held inter-corner distance error flat between 0.38 and 0.42 millimeters at all depths, while the baseline traced a U-shaped curve, accurate only within the band the calibration target had swept.

On the physical rig, a thirteen-camera array mounted on a hexagonal ceiling structure about a meter above a 2460-liter tank with an open water surface, the primary cameras achieved sub-pixel mean reprojection accuracy of 0.82 pixels, and held-out triangulated inter-corner distances showed a mean absolute error of 0.258 millimeters, a 0.43 percent mean relative error across 7,762 comparisons, with minimal systematic scale bias. The rig is not a test bed but a production platform for continuous behavioral monitoring of freely swimming Lake Malawi cichlids, supporting multi-fish 3D positioning, inter-individual distance measurements, and animal–substrate interaction analyses. Two downstream libraries, AquaMVS for refraction-aware multi-view stereo of the tank substrate and AquaPose for 3D pose estimation of swimming fish, already consume AquaCal camera models directly, and manuscripts describing them are in preparation.

The implications reach well beyond aquaria. Because the model parameterizes refraction by an arbitrary ratio of refractive indices, the pipeline extends without modification to non-aqueous media such as oil baths, with reconstruction accuracy holding across indices from 1.333 to 1.55, and a side-mounted rig imaging through a vertical interface requires no change to the model. The software, released under an MIT license with full documentation, archived input data and results on Zenodo, and Jupyter tutorials introducing both the pipeline and the refraction problem itself, replaces the improvised one-off corrections laboratories have historically relied on with a standardized, comparable implementation. The authors point to multi-layer and curved interfaces, such as dome housings, and online recalibration from tracked-animal correspondences as the most immediate extensions, both of which sit within the existing optimization architecture. For any laboratory counting on cameras to measure the underwater world accurately, the message is clear: the bending of light at the water’s surface is no longer an acceptable unknown.

Subject of Research: Refraction-aware multi-camera calibration for imaging through planar air–water interfaces

Article Title: AquaCal: refraction-aware multi-camera calibration for planar air–water interfaces

Article References: Lancaster, T., & McGrath, P. (2026). AquaCal: refraction-aware multi-camera calibration for planar air–water interfaces. SoftwareX, 36, Article 103091. https://doi.org/10.1016/j.softx.2026.103091

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103091

Keywords: camera calibration, refraction, Snell's law, 3D reconstruction, bundle adjustment, multi-camera arrays, underwater imaging, computer vision, open-source software, behavioral biology, aquaculture, ChArUco board

Developed by Jan Hejda and colleagues and described in the journal SoftwareX, BatPose is a Python package that carries a movement recording from raw stereo video all the way to biomechanical statistics. It estimates three-dimensional human pose in real physical units, computes nine joint and trunk angles, and reports range of motion, variability, and left-right symmetry, all through a graphical interface or command-line tools that run entirely on a local CPU. The software is released under the GNU General Public License, with version 1.1.0 available on GitHub, and the team has published a preliminary validation against a professional marker-based system.

The technical pipeline follows a classical stereo-geometry route rather than an end-to-end learned model. First, the user waves a ChArUco calibration board, a checkerboard embedded with fiducial markers, in front of two cameras. The software detects the board across sampled frames, deliberately keeping views that cover different regions of the image, and then solves each camera’s internal parameters and the relative position between the two cameras using Zhang’s planar calibration method. Three lens distortion models are supported, from a standard pinhole model to wide-angle and fisheye formulations, and the program warns users when the chosen model does not match the physical lens, since a mismatch can silently warp every reconstructed coordinate.

Calibration quality is graded automatically using the root mean square reprojection error: below 0.5 pixels is rated excellent, up to 1.5 pixels is acceptable, and anything higher triggers a recommendation to recalibrate, often because the lens is wider than the model allows. The developers are careful to note that this score is an in-sample statistic computed on the calibration images themselves, not a validated bound on three-dimensional accuracy at the distance where movement is later recorded. That kind of methodological honesty runs throughout the project, which distinguishes it from many flashy pose-estimation demos.

Once the cameras are calibrated, a two-dimensional pose detector locates seventeen body keypoints in each view. The default backend is MediaPipe BlazePose, with RTMPose available as an alternative, and both outputs are mapped onto the standard COCO-17 skeleton convention. The software then triangulates each keypoint in three dimensions using the direct linear transform, the workhorse algorithm of multi-view geometry. A confidence gate rejects joints whose detections fall below a threshold in either view or whose reprojection error is too large, and a One Euro filter smooths each coordinate over time with settings that users can tune to match their frame rate and the speed of the movement being studied.

A particularly thoughtful feature is the floor world frame. By triangulating the corners of a board lying on the ground and aligning them with the Kabsch algorithm, BatPose establishes an upright coordinate frame with its origin at the board center, expressed in meters. Every recording from every session then shares the same spatial reference, which makes cross-session comparisons meaningful. From the reconstructed skeletons, the package computes bilateral knee, hip, elbow, and shoulder angles as true three-dimensional geometric segment angles, plus trunk inclination relative to vertical, and derives statistics including peak angular velocity and a bilateral symmetry index originally developed for force-platform gait analysis.

The validation against a twelve-camera Qualisys marker-based system at 300 Hz gives a first quantitative picture of what to expect. Three participants performed arm raises, biceps curls, marching, forward bends, and squats under three different stereo camera configurations. Across eight flash-synchronized recordings, the frame-weighted mean per-joint position error was 52.1 millimeters, with a median of 42.1 millimeters, and 77.3 percent of joint observations met the criteria for direct measurement. Mean absolute error for the nine angle outputs averaged 7.50 degrees, with concordance correlation coefficients between 0.910 and 0.968. Notably, performance depended strongly on camera geometry: one configuration with a wider baseline and steeper convergence angle achieved errors around 45 millimeters and over 92 percent coverage, while another dropped to under 50 percent coverage.

The team also compared angle outputs against the Vicon-derived TotalCapture dataset, finding a mean recording-level error of 9.90 degrees across five participants, though with wide limits of agreement and coverage as low as 44.6 percent in some recordings. Repeatability tests showed that repeated calibrations of a fixed rig agreed to within a few millimeters of baseline and a fraction of a degree of rotation, and that repeated detections on identical input reproduced coordinates almost exactly, with occasional large discrepancies near tracking gaps. The authors repeatedly emphasize that these are preliminary technical validations under specific tested conditions, not evidence of clinical validity or generalizability to other populations, tasks, or camera arrangements.

Those caveats matter, and the limitations section is refreshingly candid. The software matches people between the two camera views by detection order rather than by identity, which means it is designed for a single subject and may fail when multiple people cross paths. Accuracy is ultimately bounded by the quality of the two-dimensional detector and the calibration, and the system reconstructs kinematics only, not the musculoskeletal forces that pipelines built on OpenSim provide. Clothing, occlusion, and camera distance were not systematically varied in controlled experiments, and derived statistics such as peak velocity and symmetry indices cannot be assumed reliable from joint-angle agreement alone. The developers plan to add cross-view identity matching and additional pose backends in future releases.

Even with those boundaries, the implications are striking. A rehabilitation clinic, a sports team, or an ergonomics laboratory in a low-resource setting could now quantify movement asymmetries, track recovery across weeks, or analyze exercise technique with equipment costing a tiny fraction of a professional capture suite, while keeping all recordings on institutional computers rather than uploading them to a cloud service. Cached intermediate files mean users can swap detectors without recalibrating and retune reconstruction without re-running detection, and a built-in camera simulator lets students learn the workflow without any hardware at all. In a field where the gap between laboratory-grade measurement and everyday practice has persisted for decades, a compact, transparent, fully local stereo pipeline with published validation numbers is a genuinely meaningful step toward democratizing the science of human movement.

Subject of Research: Markerless stereo 3D human pose estimation and biomechanical analysis software

Article Title: BatPose: Single-subject stereo 3D human pose estimation and biomechanical analysis from synchronized cameras

Article References: Hejda, J., Sokol, M., Fischer, A. G., Radus, L. F., Leová, L., Volf, P., & Kutílek, P. (2026). BatPose: Single-subject stereo 3D human pose estimation and biomechanical analysis from synchronized cameras. SoftwareX, 36, Article 103093. https://doi.org/10.1016/j.softx.2026.103093

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103093

Keywords: BatPose, motion capture, markerless pose estimation, biomechanics, stereo vision, camera calibration, MediaPipe BlazePose, triangulation, joint angles, rehabilitation, open-source software, Qualisys validation