Event-Based Vision Sensor Patents: Who Leads, Where the Gaps Are 2026
- Filing already peaked. 2017 recorded 6 filings against a total of 13 families across the whole 2015-2026 window, and the 2022 midpoint sits at just 1 — this is a field that surged early and has not repeated it.
- One family dominates citation counts. US20190197715A1 and its related filings around 'Simultaneous localization and mapping with an event camera' collect the bulk of the citation activity in this set, with the next-most-cited record over four times lower.
- Claim activity concentrates in G06T. 12 of 13 records touch G06T image data processing, while adjacent classes like aircraft equipment (B64D) and navigation (G01C) each carry a single filing — a sign of shallow exploration outside the core.
A small, early-peaking field built around SLAM
Event-based vision sensors — also called dynamic vision sensors or event cameras — report per-pixel brightness changes rather than full frames, which changes how simulation, noise modeling and pixel-level modeling need to work. The patent record around simulating and modeling these sensors is small: 13 families total, concentrated in the years around 2017. Filing has not built on that early peak; by the 2022 midpoint the annual count had fallen to a single family, and the most recent full year shows no filings from the assignees with the strongest historical activity.
The centre of gravity is a University of Zurich family on simultaneous localization and mapping with an event camera, which anchors both the citation ranking and the technical framing of the rest of the set. Everything else in the dataset — noise modeling, pixel models, sensor simulators — reads as adjacent to that SLAM problem rather than as an independent research thread.
Filing trend and technology composition
Publication lags filing by roughly 18 months, so the most recent one or two years in this trend understate real activity. Even allowing for that lag, the shape is a spike-and-fade rather than steady growth.
A 2017 peak with no second wave
Filings hit 6 in 2017, the peak of the whole window, then dropped toward the 2022 midpoint of 1. The dataset closes at 0 for 2026, though that year is still partial and the true count will settle higher once publication catches up.
G06T carries almost the entire field
Twelve of thirteen records sit in G06T (image data processing and generation), with H04N (pictorial communication) appearing three times and B64D, G01C and G06F each appearing once — evidence that simulation and modeling work here is filed as image-processing subject matter first, sensor hardware second.
Shares are the percentage of the 13 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Event-Based Vision Sensor Simulation and Modeling with Eureka
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Try EurekaThe citation leaders trace back to one SLAM family
Simultaneous localization and mapping with an event camera (US20190197715A1)
The invention relates to a method for 3D reconstruction of a scene, wherein an event camera is moved on a trajectory along the scene. The camera's pixels output events only when brightness changes occur at a given pixel, each event carrying a timestamp, a pixel address and a polarity indicating the direction of the brightness change. A plurality of successive events generated by the event camera is then used to reconstruct the scene and localize the camera along its trajectory.Filed by University of Zurich; the family is the most-cited record in this dataset by a wide margin.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20190197715A1 | Simultaneous localization and mapping with an event camera | 46 |
| 2 | WO2018037079A1 | Simultaneous localization and mapping with an event camera | 22 |
| 3 | CN114764845A | 一种模拟事件相机在太空工作的成像方法 | 4 |
| 4 | US11151739B2 | Simultaneous localization and mapping with an event camera | 1 |
| 5 | EP3504682A1 | Simultaneous localization and mapping with an event camera | 1 |
Citation counts reflect activity inside this searched corpus and favour older filings; treat them as a signal of influence on later work, not of current commercial weight.
Patent titles are shown in the language they were filed in, not translated, so that each record stays verifiable against the original filing — a translated title will not match in Eureka or in any national register. Publication numbers are shown where the record carries one (5 of 5 rows); clicking a row searches Eureka by that number.
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Three patterns stand out once the ranking and trend data are read together: an early spike that never repeated, a heavy tilt toward one IPC subclass, and a citation record dominated by a single university's SLAM work.
The surge was front-loaded
Almost half the dataset's 13 families were filed in a single year. Activity did not compound afterward, which suggests the core simulation and modeling problems that mattered in 2017 were either solved, abandoned, or absorbed into broader computer-vision filings that fall outside this search.
Claim space is narrow, not deep
A field this small still manages to cluster almost entirely in one IPC subclass. That leaves the four other subclasses touched — H04N, B64D, G01C, G06F — as thin single- or few-filing footholds rather than settled territory.
One SLAM lineage sets the reference frame
The University of Zurich SLAM-with-event-camera family and its related publications occupy the top of the citation table by a wide margin over the next-most-cited record. Later filers in this space are largely building on or distinguishing from that lineage.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to event-based vision sensor simulation and modeling, with the prior art for and against each one.
A short list of assignees, none currently active
The assignee set behind these 13 families is small, and recent-year momentum data shows the leading names — University of Zurich, MBDA UK, Zhejiang University, and the PLA Strategic Support Force Aerospace Engineering University — all recorded zero filings in the latest tracked year. That does not necessarily mean exit; publication lag means recent work may not yet be visible.
University of Zurich
Holds the most-cited family in the dataset, covering 3D reconstruction and localization using event-camera pixel-level event streams. Its framing of timestamped, polarity-tagged events set the technical vocabulary much of the rest of the field builds on or around.
MBDA UK
Appears against the B64D aircraft-equipment classification, one of the few records in this dataset that ties event-camera modeling to an airborne platform context rather than general robotics or SLAM.
Zhejiang University
Contributes to the small cluster of Chinese-origin filings in this set, alongside work from PLA Strategic Support Force Aerospace Engineering University on modeling event-camera imaging in space operating conditions.
| Assignee | Recent year | YoY |
|---|---|---|
| University of Zurich | 0 | — |
| MBDA UK Limited | 0 | — |
| Zhejiang University | 0 | — |
| PLA Strategic Support Force Aerospace Engineering University | 0 | — |
Where to take this analysis
The dataset is small enough to read in full, but that also means conclusions about competitive position need testing against adjacent searches before they inform a filing decision.
Check the SLAM-adjacent broader corpus
This search restricts to specific IPC codes and title/claim terms; broader computer-vision or robotics-SLAM filings from the same assignees may sit just outside this set and change the momentum picture.
Explore in Patsnap EurekaWatch for the publication-lag correction
Zero filings in the most recent year is consistent with normal 18-month publication delay rather than confirmed exit from the field; revisit this trend once later years fill in.
Explore in Patsnap EurekaMap the receiving-office spread against target markets
Filings split across EPO, China, UK, US, WIPO and Austria in small numbers each — worth checking which offices matter for your intended commercial territory before assuming any single jurisdiction dominates.
Explore in Patsnap EurekaCommon questions about this landscape
This dataset identifies 13 patent families published between 2015 and mid-2026 that match search terms around event camera simulators, sensor noise modeling and pixel models, filtered to specific image-processing and vision IPC codes. That is a small, specialized set rather than a full picture of all event-camera patents; broader searches on event camera hardware or general SLAM would return far more. Treat this figure as the size of the simulation-and-modeling sub-niche specifically, not the whole event-vision patent space.
The most cited family in this dataset originates from University of Zurich, covering simultaneous localization and mapping with an event camera. Other identified assignees include MBDA UK, Zhejiang University, and PLA Strategic Support Force Aerospace Engineering University, each associated with a single family in this set. None of these four shows filings in the most recent tracked year, though publication lag means that could reflect reporting delay rather than an actual pause in R&D.
Filing activity peaked in 2017 with 6 families, roughly half of the entire 13-family dataset, and had fallen to just 1 by the 2022 midpoint. The trend through 2026 shows no recovery to the 2017 level, though the most recent year is still partial due to the roughly 18-month gap between filing and publication. On the evidence available, this looks like a field that surged once around a foundational SLAM concept and has not generated a comparable second wave since.
US20190197715A1, assigned to University of Zurich, claims a method for 3D scene reconstruction using an event camera whose pixels output timestamped, polarity-tagged events only when brightness changes occur, with successive events used to localize the camera along its trajectory. It is the most-cited record in this dataset by a wide margin. Anyone building event-camera SLAM or reconstruction methods that rely on that same event-stream structure should review its claim scope closely, though narrower simulation or noise-modeling work that does not perform localization from the event stream may fall outside it.
The IPC composition shows 12 of 13 records concentrated in G06T image data processing, leaving classes like B64D (aircraft equipment), G01C (navigation) and G06F (general digital data processing) with only one filing each. That points to under-claimed combinations such as event-camera noise modeling tuned for space or airborne imaging conditions, and pixel-level models built specifically for navigation-grade rather than general-purpose applications. These are narrow openings in a small dataset, so they warrant a freedom-to-operate search rather than being treated as confirmed gaps.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
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Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.