The research, led by Huaqing Huang and Naifei Liu of the School of Design at Shanghai Jiao Tong University, together with colleagues from Tsinghua University, the University of Virginia, and industry partners, tackles a problem that has long divided the virtual heritage community. On one side stands scientific rigor: reconstructions must be grounded in verifiable evidence, from archaeological surveys to historical documents, and must clearly distinguish what is known from what is inferred. On the other side stands contextual narrative: an immersive display must feel complete and coherent, giving visitors a believable sense of place rather than a scattering of fragmentary, heavily caveated ruins. The authors describe these two dimensions as decoupled in much existing practice, and their central contribution is a workflow designed to reunify them.

That workflow unfolds in four sequential stages: data collection and processing, spatial scripting, virtual reconstruction, and asset production. Each stage addresses a distinct technical challenge. Data collection aggregates the heterogeneous evidence base, including surveying data, heritage documentation, maps, and interdisciplinary historical materials. Spatial scripting then organizes this material into a structured plan for the virtual environment, determining how spaces relate to one another and how a visitor’s journey through them will unfold. Virtual reconstruction translates the scripted spatial logic into three-dimensional form, while asset production refines the models, textures, and interactive elements that make the environment usable in a real-time VR platform.

One of the most consequential questions the team confronted was prioritization. Urban heritage on the scale of Jingdezhen’s porcelain industry cannot be reconstructed in exhaustive detail everywhere at once; computational budgets, production timelines, and the limits of historical evidence all impose constraints. The study therefore addresses how to determine which elements of the 3D recreation deserve the highest fidelity and which can be represented more schematically, in order to balance scientific validity against contextual completeness. This triage process, the authors argue, is where interdisciplinary judgment matters most: archaeologists, historians, conservation specialists, and digital designers must negotiate together what the virtual city should emphasize and where approximation is acceptable.

The case at the heart of the project is the core heritage area of Jingdezhen’s porcelain industry, the zone where kilns, workshops, and the commercial infrastructure of ceramic production were most densely concentrated. Reconstructing an industrial urban landscape poses particular difficulties compared with the monuments that dominate virtual heritage work. Industrial heritage is often fragmentary, with above-ground remains that are partial, altered, or buried, and its significance lies as much in processes and spatial relationships as in individual structures. Conveying how clay moved through a production chain, how kilns were organized within the city, and how workers and merchants inhabited these spaces requires a reconstruction strategy that encodes function, not just geometry.

The scale of the collaboration is itself notable. The project involved an interdisciplinary team of more than twenty experts and spanned over a year of work. The acknowledgements reveal the breadth of institutional support required: the Jingdezhen Ceramic Culture Tourism Group, known as Taowenlyu, provided early surveying data and helped coordinate implementation on the display platform; Tsinghua University and the Qingyuan Conservation team supplied key heritage data and academic consultation; and Zhongxin Digital Culture defined system technical requirements while advancing technical implementation and operational activities in parallel. The research itself was supported by the Spatial Intelligence and Virtual-Real Fusion Design Laboratory at Shanghai Jiao Tong University, where the academic core of the team is based.

This division of labor reflects a broader trend in digital heritage, where universities, cultural institutions, and technology companies increasingly collaborate on projects that no single organization could deliver alone. Heritage datasets of the kind held by conservation teams are rarely formatted for real-time rendering; game-engine-ready environments demand optimization decisions that can quietly alter historical content if made without expert oversight. The Jingdezhen workflow formalizes the handoffs between these communities, making explicit where historical interpretation ends and technical production begins, and building checkpoints into the pipeline where domain specialists can verify that the emerging 3D environment remains faithful to the evidence.

The integration of interdisciplinary historical materials, which the paper identifies as a core question of the process, is arguably the methodological heart of the work. Historical sources for a city like Jingdezhen arrive in incompatible forms: textual records, historic maps, archaeological excavation reports, architectural surveys, photographs, and oral accounts of craft practice. Each carries its own scale, precision, and bias. Fusing them into a single coherent spatial model requires decisions about alignment, dating, and confidence that are as much historiographical as they are technical. The study’s treatment of this integration stage suggests a model for other cities pursuing similar projects, particularly those whose heritage value lies in urban patterns and industrial systems rather than in freestanding architecture.

The timing of the project is significant as well. Jingdezhen’s pursuit of World Cultural Heritage status has made the legible presentation of its porcelain industry a matter of international importance, and immersive VR display offers a way to communicate that value to audiences who cannot physically access fragile sites or who would otherwise see only fragmentary remains. Virtual reconstruction, in this framing, is not merely a visualization exercise but an argument: it assembles scattered evidence into a spatial narrative that makes the case for the city’s outstanding universal value. The VR project described in the study was developed explicitly as part of that bid, positioning digital heritage tools within the formal machinery of UNESCO nomination processes.

The authors are candid that the approach carries challenges alongside its potentials. Virtual recreation at urban scale remains expensive, slow, and dependent on the availability and quality of underlying data. Interpretive choices made during spatial scripting and reconstruction can harden into apparent fact once rendered in convincing three dimensions, a risk the field of virtual heritage has debated for decades. By publishing the workflow, the team aims to make those choices visible and repeatable, so that other projects can adopt, critique, and improve the method. The article, published open access in npj Heritage Science on 11 October 2026, arrived as a peer-reviewed accepted manuscript carrying a permanent DOI, and its authors declare no competing interests. As cities worldwide grapple with how to document industrial and urban heritage threatened by development, decay, and climate pressures, the Jingdezhen project offers a template: a disciplined pipeline that treats historical evidence, narrative design, and real-time technology as equal partners in the work of keeping a vanished city alive in memory and in silicon.

Subject of Research: Virtual reality reconstruction of the urban porcelain-industry heritage of Jingdezhen, China

Article Title: Virtual recreation of urban heritage of Jingdezhen for immersive VR display

Article References: Huang, H., Liu, N., Zhang, K., Wang, S., Zhu, S., Yue, K., & Li, Z. (2026). Virtual recreation of urban heritage of Jingdezhen for immersive VR display. npj Heritage Science. https://doi.org/10.1038/s40494-026-03036-y

Image Credits: AI Generated

DOI: 10.1038/s40494-026-03036-y

Keywords: virtual reality, digital heritage, Jingdezhen, porcelain industry, urban heritage, 3D reconstruction, World Cultural Heritage, npj Heritage Science, spatial scripting, immersive display, cultural heritage, Shanghai Jiao Tong University

To understand why this matters, it helps to consider what OCT actually does. The technique works a bit like ultrasound, except it uses near-infrared light instead of sound. A broadband or rapidly swept laser illuminates the sample, and an interferometer compares the weak light reflected from different depths within the sample against a reference path. By analyzing the interference pattern, the system reconstructs a depth profile with micrometer-scale axial resolution. Sweeping the beam across the surface builds up cross-sectional images, and stacking those slices yields full three-dimensional volumes. Because the method is entirely contactless and relies on low-power light rather than ionizing radiation, it can inspect delicate, soft, or transparent materials without deforming or damaging them, a property that becomes invaluable when the object under test is a photoresist layer that a physical probe would smear.

The catch has always been the hardware. A laboratory OCT system typically sprawls across an optical table: a bulky swept laser, discrete fiber couplers, a reference arm with moving parts or modulators, balanced photodetectors, and a maze of alignment-critical connections. That complexity translates into cost, fragility, and maintenance overhead, which explains why OCT has penetrated hospitals far more successfully than production lines. Photonic integrated circuits offer a way out, promising to fold the interferometer, the detectors, and eventually the light source onto chips that can be fabricated by the thousands in semiconductor foundries. But the field has struggled with two persistent problems: choosing a material platform that foundries can manufacture reliably at low optical loss, and designing a flexible sample arm so the chip-bound engine can still reach real-world objects.

The Shanghai Jiao Tong team, led by corresponding author Xingchen Ji, attacked both problems simultaneously. Their chip integrates low-loss silicon nitride interferometers with germanium photodiodes on a commercially fabricated silicon-based platform, combining the distinct strengths of multiple photonic integration technologies in a single device. Silicon nitride is prized in photonics for its extremely low propagation loss and broad spectral transparency, making it ideal for the interferometric heart of an OCT engine. Germanium, meanwhile, absorbs near-infrared light efficiently and serves as the natural photodetector material on silicon. The challenge is that these waveguide platforms have different geometries and optical modes, so the researchers developed an interlayer coupling structure to transfer light between the silicon nitride and silicon layers with a coupling loss of less than 0.15 decibels, a figure low enough to preserve the interference contrast on which OCT sensitivity depends.

The result is a remarkably dense consolidation of optical function. The interferometer and photodetectors occupy a footprint of just 0.9 by 0.4 millimeters, yet the design deliberately keeps the sample arm external, connected through fiber. That architectural choice preserves flexibility: the same chip-based engine can drive different probes depending on the inspection task. Ji emphasized the intent behind the design, saying the aim was to create a compact OCT module adaptable to practical industrial inspections. Integrating the interferometer and photodetectors on-chip reduces the number of discrete optical components, he noted, while the fiber-connected sample arm significantly expands the system’s operational range. In other words, the chip handles the precision optics, and the fiber handles the reach.

Performance figures from the experimental testing suggest the miniaturization did not come at the expense of capability. The system achieved 87 decibels of sensitivity, a measure of its ability to detect weakly reflected light from deep within a sample, along with a 3.42-millimeter sensitivity roll-off range, meaning usable signal persists over a respectable imaging depth. The team resolved internal structures within a smartphone camera module, demonstrating that the chip can inspect the kind of densely packaged, multi-layer consumer electronics that are notoriously difficult to verify non-destructively. Thickness measurements of a silica wafer, compared against established contact-based methods, showed absolute errors below 2 micrometers, while measurements of soft SU-8 photoresist blocks yielded relative errors under 0.12 percent. For semiconductor and microfabrication workflows, where film thicknesses are specified to sub-micrometer tolerances, that level of agreement with reference techniques is the difference between a laboratory curiosity and a production tool.

Perhaps the most striking demonstration involved a probe barely wider than a human hair is thick. By connecting an angle-polished ball-lens fiber probe with a diameter of just 450 micrometers, the researchers captured three-dimensional images of threads hidden inside a metal optical post and of internal layers within a roll of plastic tape. The ball lens focuses the emitted light to a tight spot while the angled polish suppresses back-reflections that would otherwise swamp the weak signal from the sample. This plug-and-play attachment effectively turns the chip-based engine into a miniature endoscope for machines, capable of inspecting bores, threads, and sealed assemblies that no conventional microscope objective can physically access.

Hang Su, the study’s first author and a doctoral student at Shanghai Jiao Tong University, highlighted why contactless operation matters so much in practice. Contactless measurement is invaluable when physical contact could deform a soft surface, Su explained, and the fiber probe allows the team to examine structures that are inaccessible to conventional microscopes. Describing the platform as a plug-and-play OCT module, Su said it paves the way for the next generation of chip-scale OCT engines. That framing captures the broader vision: rather than building a monolithic instrument for one task, the researchers envision a standardized photonic core to which different probes and sample arms can be attached as easily as swapping a lens on a camera.

The commercial implications extend beyond the demonstrations themselves. Because the chip was fabricated on a commercially available silicon photonics platform, the design is compatible with foundry-scale production, which is the prerequisite for driving down OCT system costs to levels that mid-sized manufacturers could justify. The researchers note that complementary technologies, including embedded III-V semiconductor lasers on silicon-on-insulator wafers and chip-based silicon nitride tunable delay lines, could be incorporated to realize a fully integrated, miniaturized OCT system in which even the swept light source lives on the chip. The system described in the paper already integrates a III-V semiconductor light source, silicon nitride interferometers, silicon-germanium photodetectors, and the ball-lens microprobe, drawing on the advantages of each material platform. A Chinese provisional patent application has been filed regarding the technology, signaling the team’s intent to move from demonstration toward deployment.

If that transition succeeds, the implications could reach well beyond metrology labs. Inline, non-destructive inspection of coatings, adhesives, semiconductor packages, medical device surfaces, and additive-manufactured parts could shift from sampling-based quality checks to continuous, three-dimensional verification. The same chip-scale engines could find uses in machine vision systems that need depth information at micrometer resolution, or in compact sensors embedded directly into production equipment. What the Shanghai Jiao Tong team has shown is that the optical complexity that once confined OCT to the optical table can be compressed onto a chip the size of a printed period, without sacrificing the sensitivity or accuracy that made the technique worth shrinking in the first place. The factory floor, long the harder market for optical coherence tomography, may finally be within reach.

Subject of Research: Chip-based photonic integrated circuit for swept-source optical coherence tomography in industrial inspection

Article Title: Compact photonic chip enables three-dimensional OCT inspection for industrial samples

Article References: Compact photonic chip enables three-dimensional OCT inspection for industrial samples. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: optical coherence tomography, photonic integrated circuits, silicon photonics, silicon nitride, industrial inspection, non-destructive testing, swept-source laser, ball-lens fiber probe, 3D imaging, metrology, germanium photodiodes, Shanghai Jiao Tong University

The core contribution of the work is a neural architecture the authors call the Occlusion-Aware Pairwise Relational Network, or OAPRN. Rather than trying to understand an entire cluttered pile of objects in one monolithic computation, the network breaks the problem down into a sequence of pairwise judgments. For every pair of objects detected in the scene, the network answers a deceptively simple question: which one of the two is on top? This might sound like a crude simplification of the full three-dimensional reasoning problem, but the mathematics of stacking relationships is friendly to such decompositions. If object A occludes object B, and object B occludes object C, then a robot can infer that A must be dealt with before C, even if A and C never interact directly in the image. Stacking, in other words, is a transitive relation, and pairwise comparisons encode all the information needed to recover the full ordering.

This pairwise decomposition is not just an aesthetic choice; it carries real algorithmic consequences. When a relational network must process all objects and their interactions simultaneously, the size of its internal representation grows combinatorially with the number of objects, and training the network requires examples covering every conceivable clutter configuration. By contrast, a pairwise network sees a much narrower and more consistent learning problem: given two objects and their image evidence, predict the occlusion direction. Because each training example concerns just two objects, the diversity of scenes needed to teach the network the underlying visual cues is far smaller, and the learned features generalize more readily to piles with different numbers of items. Once individual pairwise predictions are made, the system assembles them into an overall relationship graph, a directed structure whose edges point from the covering object to the covered one, and from which the full stacking hierarchy of the scene can be read off.

The graph itself is where perception turns into action. With the relationship graph in hand, a robot asked to grasp a specific target object can trace the chains of occlusion leading to that object and identify exactly which items stand between the gripper and the goal. The grasping strategy then proceeds in a deliberately orderly fashion: objects that cover the target are removed first, beginning with those highest in the stack, and the target is grasped only once its immediate surroundings are clear. This sequential clearing avoids the classic failure mode of clutter manipulation, in which a greedy grasp of the target dislodges neighbors and buries it deeper. The authors describe how the robot can execute this plan quickly, because the computational cost of the pairwise network remains modest even as the pile grows, and because the graph reconstruction step operates on a small number of detected objects rather than on raw image data.

Efficiency is not merely a convenience in this setting; it is what allows the robot to cope with the fundamental non-determinism of manipulation. Every time a gripper removes an object, the remaining pile shifts, occlusions change, and previously hidden surfaces come into view. A system that needed several seconds to recompute the scene graph would be constantly planning against a world that no longer exists. Because OAPRN can rebuild the relationship graph rapidly from a fresh image, the robot can replan after every action, treating each removal as a new observation rather than a disruption to a fragile long-horizon plan. This closed-loop quality, in which perception, planning and action alternate at a pace matched to the physical dynamics of the clutter, is arguably the most practically important feature of the method, and it distinguishes the approach from heavier relational reasoning pipelines that produce accurate but stale scene interpretations.

The technical pipeline begins with standard object detection on the input image, for which the authors leverage modern detector tooling of the kind exemplified by the widely used YOLO family of models cited in their bibliography. Each detected object yields an image region and associated features, and these pairwise feature combinations are passed through the relational network to predict occlusion direction. The idea of detecting visual manipulation relationships in this way has a lineage in the robotics literature, notably in work on visual manipulation relationship networks and gated graph neural networks for robotic grasping, as well as graph-based reasoning for target-driven grasping in dense clutter. What the Shanghai Jiao Tong team adds is a focus on making this reasoning lightweight enough for real-time, iterative use, and on coupling it directly to a principled clearing strategy rather than to learned push-and-grasp policies that can be harder to interpret and verify.

The experimental section of the paper puts the method through its paces in cluttered scene trials designed to verify both the relationship detection and the resulting grasp behavior. The authors report that experiments were carried out to verify the proposed method, and the accompanying figures trace the pipeline from raw input images through pairwise relationship analysis, graph construction, and the executed sequence of removals culminating in the target grasp. A supplementary video accompanies the publication, offering a dynamic view of the system operating on physical clutter. The work was supported by the National Science and Technology Major Project under grant number 2024ZD1600204, and the authors note that no datasets were generated or analyzed during the study beyond those used in the reported experiments. Author contributions attribute the algorithm design to Yan Zha and Yuxuan Wang, the experimental work to Zha, Wang and Jianhua Wu, and the manuscript drafting to Zha, with all authors reviewing the final text.

Situating this work in the broader landscape of robotic grasping research clarifies why it matters. Grasp detection as a field has advanced enormously, from early deep learning approaches to grasp rectangles through modern six-degree-of-freedom grasp planners operating on point clouds, and systems such as AnyGrasp that perceive grasps robustly across space and time. Yet most of these methods answer the question of how to grasp an object, not whether it should be grasped yet. A parallel line of research, including work on target-oriented push-grasping synergies and hierarchical stacking relationship prediction, has tackled the order-of-operations problem, often at considerable computational expense or with limited adaptability to scene changes. Large vision-language-action models promise general manipulation competence, but their inference cost makes them poorly suited to the millisecond-level replanning loop that clutter demands. OAPRN occupies a pragmatic middle ground: a specialized, efficient relational reasoner that does one thing, determining what covers what, well enough to support fast and safe sequential manipulation.

The potential applications extend across both halves of the robotic world the authors name in their motivation. In industrial automation, random bin picking remains one of the highest-volume manipulation tasks in logistics and manufacturing, and any improvement in the speed or reliability with which a robot can extract a specific part from a tangled pile translates directly into throughput. In domestic service, where robots must fetch requested items from drawers, refrigerators, and countertops they have never seen arranged in advance, occlusion-aware reasoning is the difference between a helpful assistant and a hazard. The decomposition strategy also has an appealing quality for safety-critical settings: because the relationship graph is explicit and human-readable, an engineer can inspect exactly why the robot decided to remove one object before another, rather than trusting an opaque end-to-end policy. As robots move from structured cells into the genuinely messy environments where humans live and work, research of this kind, focused on making relational perception fast, transparent, and reactive, may prove as consequential as the grasp planners and foundation models that currently dominate the headlines.

Subject of Research: Occlusion-aware pairwise relational reasoning for efficient robotic grasping of target objects in cluttered scenes

Article Title: Efficient grasping of target objects in cluttered scenes

Article References: Zha, Y., Wang, Y., Wu, J., & Xiong, Z. (2026). Efficient grasping of target objects in cluttered scenes. International Journal of Intelligent Robotics and Applications. https://doi.org/10.1007/s41315-026-00595-y

Image Credits: AI Generated

DOI: 10.1007/s41315-026-00595-y

Keywords: robotic grasping, occlusion detection, relational networks, cluttered scenes, graph-based planning, robotic manipulation, object relationship detection, pairwise reasoning, computer vision, machine learning, industrial automation, Shanghai Jiao Tong University