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Run your analysis now →https://www.patsnap.com/resources/blog/rd-blog/lidar-point-cloud-processing-patent-landscape/ · Patsnap · data cut-off 2026-07-31 · downloaded from the live page
Patent Landscape · LidarLidar Point Cloud Processing Patents: Who Files, What They Claim
- 34.9% of all 2,741 records sit with just five assignees, so the ground segmentation and clustering claim space is already dense at the top before you even look at the long tail.
- Filings peaked at 532 in 2023 and the 2021→2024 span shows a -16% pull-back, though 2025-26 counts are still filling in given the usual 18-month publication lag.
- G01S and G06T lead composition at 28.6% and 23.6% of records respectively, but B60W joint vehicle-control claims still touch over a fifth of the corpus, showing how tightly point cloud work is bound to drive-control filings.
2,741
Published Records
35%
Top-5 Share of All Records
-16%
Filing Growth 2021→2024
US
Leading Jurisdiction
Filing growth compares 2021 (359 records) with 2024 (300) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 2,741 records in scope (CR5), not by the ranked leaders only.
Published byPatsnap Research··7 min readSourced from Patsnap Eureka
Overview
What this landscape covers
This landscape tracks patent filings at the intersection of lidar point cloud generation and the processing steps that turn raw returns into usable objects: ground segmentation, clustering, sensor fusion, motion compensation, and the compute-latency and annotation-cost problems that come with running these pipelines at scale. The scope spans 2,741 published records filed or published between 2015 and the 2026 data cut-off.
Records are drawn from filings that explicitly combine point cloud or object-detection lidar language with a downstream processing step, so the set captures the engineering layer between sensor output and perception decisions rather than lidar hardware alone.
Filing activity and technology composition, 2015-2026
Source: Patsnap Eureka. Assignee ranking and totals. Derived from a Patsnap search on Lidar Point Cloud Processing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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The data
Filing trend and technology composition
Two views of the same 2,741 records: how filing activity has moved year over year, and which IPC subclasses carry the work.
Filing trend, 2017-2026
Annual publications rose from 91 in 2017 to a peak of 532 in 2023. The 2021-to-2024 window shows a -16% change (359 to 300), which reads as a genuine pull-back rather than noise since both years are past the publication lag window; 2025 and 2026 figures will keep revising upward as later filings publish.
Technology composition by IPC subclass
G01S (radar, sonar and positioning) and G06T (image data processing) are the two largest classes, at 28.6% and 23.6% of the 2,741 records respectively. G06V recognition and B60W joint vehicle control each clear a fifth of the corpus, and G06N AI-model computing sits close behind — evidence that point cloud processing claims are rarely written in isolation from perception and control claims. Because records can carry multiple classes, these shares sum to well over 100%.
Shares are the percentage of the 2,741 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Source: Patsnap Eureka. Filing trend and technology composition. Derived from a Patsnap search on Lidar Point Cloud Processing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.
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A representative filing
Representative record
Point cloud motion compensation method and apparatus, storage medium, and lidar (US20230141421A1)
SUTENG INNOVATION TECHNOLOGY CO., LTD.
The filing covers a method for compensating point cloud motion by combining millimeter-wave radar point cloud data, timestamped and carrying a motion parameter for a target object, with a separately obtained lidar point cloud data frame from scanning the same working region. The two data sources are aligned to correct for the time-of-flight and motion artefacts that arise when a lidar performs a rotating or scanning capture while the target itself is moving.Filed by Suteng Innovation Technology; published 2023-05-11.
Most-cited records in scope
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20190258251A1 | Systems and methods for safe and reliable autonomous vehicles | 1,044 |
| 2 | US20200284883A1 | Component for a lidar sensor system, lidar sensor system, lidar sensor device, method for a lidar sensor syst… | 1,013 |
| 3 | US20210157312A1 | Intelligent vibration digital twin systems and methods for industrial environments | 715 |
| 4 | US20210342836A1 | Systems and methods for controlling rights related to digital knowledge | 464 |
| 5 | US20220366494A1 | Market orchestration system for facilitating electronic marketplace transactions | 437 |
| 6 | US20220108262A1 | Industrial digital twin systems and methods with echelons of executive, advisory and operations messaging and… | 386 |
| 7 | US20160068267A1 | Context-based flight mode selection | 356 |
| 8 | US20210133670A1 | Control tower and enterprise management platform with a machine learning/artificial intelligence managing sen… | 339 |
| 9 | US20230123322A1 | Predictive Model Data Stream Prioritization | 337 |
| 10 | US20220187847A1 | Robot Fleet Management for Value Chain Networks | 336 |
Citation counts favour older filings simply because they have had longer to accumulate citations inside this corpus; treat them as a signal of influence, not of current technical importance.
Each row carries its publication number; clicking a row searches Eureka by that number.
Source: Patsnap Eureka. Citation counts and representative records. Derived from a Patsnap search on Lidar Point Cloud Processing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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What the numbers mean for a filing decision
Read together, the concentration figures, the trend, and the receiving-office split point to a field where the core motion-compensation and fusion claims are already staked out, while procedural and cost-reduction angles remain more open.
Concentration
34.9%
of 2,741 records held by top 5 assignees
The top of the field is dense
Five assignees hold 956 of the 2,741 records in scope, 34.9% of the total. That concentration sits mostly in core detection and motion-compensation claims tied to autonomous-vehicle programs, which makes those specific claim constructions expensive ground to file into without a clear design-around.
Top 5 combined, 956 of 2,741 records
Momentum
-16%
change in filings, 2021 to 2024
Growth has cooled from its 2023 peak
Annual filings peaked at 532 in 2023, and the last fully comparable span shows a -16% move from 359 filings in 2021 to 300 in 2024. That is a real pull-back in a maturing filing base, not a data artefact, though the 2025-26 counts shown elsewhere on this page will still revise upward as later filings publish.
2021: 359 → 2024: 300
Composition
28.6%
of records classed under G01S
Positioning and imaging classes dominate
G01S (radar, sonar and positioning) leads at 28.6% of the 2,741 records, closely trailed by G06T image-data processing at 23.6%. The overlap with B60W vehicle-control classes at 21.1% shows most point cloud processing patents are written as part of a larger autonomous-driving claim set rather than as standalone sensing patents.
G01S 784 records, G06T 646 records
Jurisdiction
1,657
records with a US receiving office
Filing activity is US-centred
The United States receives more than five times the filings of the next-largest office (EPO, 318), with PCT applications at 266. Anyone building a global filing strategy in this space should expect the densest prior art, and the sharpest competitive activity, in US filings first.
US 1,657, EPO 318, WIPO/PCT 266
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Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to lidar point cloud processing, with the prior art for and against each one.
Source: Patsnap Eureka. Co-assignee relationships and derived observations. Derived from a Patsnap search on Lidar Point Cloud Processing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
Players
Who is filing, and where the field is still open
The ranked leaders are dominated by autonomous-vehicle platform companies and their component suppliers, with a long tail of single- or few-filing entrants behind them.
Leader
377
records
One assignee holds a clear lead
The top-ranked assignee holds 377 records, well ahead of the fifth-place assignee at 94 and the tenth-place assignee at 70. That gap between first place and the rest of the top ten suggests one program has filed far more broadly across the point cloud processing stack than any single competitor.
Leader: 377 records; 10th place: 70 records
Mid-field
49.3%
of 2,741 records held by top 10
The top ten already cover half the field
The ten highest-ranked assignees together hold 1,352 of the 2,741 records in scope, 49.3% of the total. Filing into this space now means either designing around that half of the corpus or picking a sub-area the leaders have not yet claimed densely.
Top 10 combined, 1,352 of 2,741 records
Momentum
-71% YoY
in latest-year filings for one leading assignee
Recent-year activity is cooling across the board
Several of the most active historical filers show falling or flat latest-year counts, with year-on-year drops as steep as -71% for one and -100% for another. Given the publication lag, this understates true recent activity, but it is consistent with a field where the earliest movers have already staked their core claims and are filing less broadly now.
Latest-year filings down across multiple ranked leaders
Recent-year filing momentum
| Assignee | Recent year | YoY |
|---|---|---|
| STRONG FORCE TX PORTFOLIO 2018 LLC | 2 | -33% |
| NVIDIA Corp | 2 | -71% |
| Waymo LLC | 1 | -67% |
| GM Cruise Holdings LLC | 0 | — |
| Strong Force VCN Portfolio 2019 LLC | 0 | -100% |
| TuSimple Inc | 0 | — |
| Motional AD LLC | 0 | -100% |
| GM Global Technology Operations LLC | 0 | -100% |
Source: Patsnap Eureka. Assignee-level momentum. Derived from a Patsnap search on Lidar Point Cloud Processing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
What's next
Where to take this analysis
The figures above describe the field as it stands at this data cut-off. Turning that into a filing or freedom-to-operate decision means going claim by claim.
Map claims against the leader's portfolio
Start with the 377-record leader and the next four ranked assignees, since together they hold 34.9% of all records in scope, and check which specific claim language recurs across their filings.
Explore assignee portfolios in EurekaTest a white-space claim before drafting
Annotation-cost reduction and compute-latency optimisation show thinner, more specific coverage than core detection claims; running a candidate claim against the corpus early avoids drafting into occupied space.
Run a claim search in EurekaSource: Patsnap Eureka. Forward-looking reading of the same dataset. Derived from a Patsnap search on Lidar Point Cloud Processing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
FAQ
Common questions about this landscape
One assignee leads the ranked field with 377 records, noticeably ahead of the fifth-place holder at 94 and the tenth-place holder at 70. The top five assignees combined hold 956 of the 2,741 records in scope, 34.9% of the total, so the leading edge of this field is concentrated in a handful of autonomous-vehicle platform companies and their suppliers. Below the top ten, which together hold 49.3% of records, the field spreads into a long tail of assignees with far fewer filings each.
Filings rose steadily from 91 in 2017 to a peak of 532 in 2023. Looking at the most recent fully comparable years, filings fell from 359 in 2021 to 300 in 2024, a -16% change, which points to a genuine cooling rather than continued acceleration. Counts for 2025 and 2026 are still low in the data because publication typically lags filing by around 18 months, so those years will revise upward over time and should not be read as a further decline yet.
The two largest IPC subclasses are G01S, covering radar, sonar and positioning, at 28.6% of the 2,741 records, and G06T, covering image data processing and generation, at 23.6%. G06V image and video recognition and B60W hybrid or joint vehicle control each cover more than a fifth of records, showing that point cloud processing claims are typically bundled with recognition and vehicle-control claims rather than filed as pure sensing patents. Because a single record can carry several IPC classes, these shares add up to more than 100% and should not be summed.
The densest claim coverage sits in core object-detection and motion-compensation methods tied to autonomous-vehicle platforms, where the top-ranked assignees already hold a third of all records. Thinner coverage appears in annotation-cost reduction for labelling point cloud training data, compute-latency optimisation for onboard clustering, and cross-sensor motion-compensation calibration, all of which touch the same processing pipeline without duplicating the heaviest-filed claim language. A freedom-to-operate search focused on those specific sub-areas is more productive than trying to file around the core detection claims directly.
US20230141421A1, filed by Suteng Innovation Technology and published 2023-05-11, covers a method for compensating point cloud motion by fusing timestamped millimeter-wave radar point cloud data carrying a target's motion parameter with a separately captured lidar point cloud data frame from the same working region. The method aligns the two data sources to correct for motion and scan-time artefacts that distort a moving target's position in the lidar frame. Anyone building a motion-compensation pipeline that fuses radar and lidar point clouds in this specific way should review this filing's claim scope before finalising an implementation.
Answers are grounded in the same dataset. Derived from a Patsnap search on Lidar Point Cloud Processing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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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.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
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.