Ever wondered why your LiDAR or mmWave radar starts throwing ghost points or drifting on a hot summer day?

Here's the reality:

Most automotive radars run 24/7. Under direct sun + cabin heat soak, core chip temps often exceed 85°C. And when that happens:

• Detection accuracy drops
• Signal drifts
• Point cloud goes noisy
• Failure rate spikes

Passive cooling? Air cooling and thermal pads are too slow, too uneven, and way too bulky for today's compact radar designs.

We're Pioneer Thermal. After a lot of trial-and-error, we took a different approach — a full-range liquid cold plate that actually works in the real world.

What we did:

• Precision stamping + vacuum brazing
• Optimized micro-channels for uniform heat exchange

Real test results (on-vehicle validated):

• Core temp < 56°C steady
• Plate-wide temp difference ±3°C
• Heat dissipation improved by 60%

And it's not a lab toy. It survived:

• -40°C to 85°C thermal cycling
• Vibration and hydraulic leakage tests (vehicle-grade)
• Thin-wall lightweight design — fits roof / front cabin embedded layouts

Pioneer Thermal also handle the full chain in-house: friction stir welding, brazing, assembly, thermal simulation, structural customization, and interface matching. Non-standard custom and mass delivery are both supported.

If you're working on radar thermal management and want to see the data, happy to share more.

Drop a comment or DM — always open to geeky engineering discussions.

I have been working on a project for the last 5-6 months, but I always hit a problem when I try to parse commercial floor plans (schools, universities, shopping malls, airports, etc.). The datasets I have tried out are Cubicasa5k and Modern Swiss dwelling, but the main problem that I face while using these datasets are they are actually made of residential floor plans, so when I train my ML model, the parsing does not actually work the way I intend it to; the error percentage is way too high to even consider the output usable

Although I have gotten around this specific problem by parsing one image in segments and then stitching them one by one, this does not really seem like a viable step to do, and even that solution has some gaps in it

So I am fully down to make my own dataset and annotate it and then use that data to train my model, but I do not know how to deal with copyright issues, and also where I can obtain floor plans of buildings that already exits irl; where can I get the resources that I want?

If anyone has any idea of how to obtain them, please help !!

I have been trying to use photogrammetry to scan small objects (smaller than a golf ball) to convert to STL for printing without much luck, thanks to the narrow depth of field.

Will LiDAR do it for me? I am not having much luck finding an answer.

If I were to buy something like a Lidox mid-70, would it be able to accurately scan a seashell? If so, what would be required to process that information and turn it into an STL file? (I suspect that part won't be easy)

Thanks in advance for any assistance.

Just some info about them and my wip project. Might be useful to some.

Hello!

I'm a complete beginner with LiDAR and QGIS. I have a single LAZ file from the USGS 2024 Lincoln County QL1 project. Family lore and handwritten maps indicate that my grandparents and early family (including my father) lived in a rented dwelling in the 1940s in a logging camp in that area.

A few months ago I went up to the area (48.24010° N, 115.56310° W) and explored on foot. There are some features (such as the intersection of Smearl Creek and the forest service road) which correspond exactly to the map and memories of my oldest cousins who had visited in the 70s. Other features do not (for example, nobody recollects steep terrain off the road but now from the road and from the creek are very steep and significant terrain). Besides that, there's been 80 years of storms and pine needles, my wife and I couldn't find any proof of settlements (although we found a cedar grove where the handwritten map had one sketched in).

There's another access road 300-500' north of the point. The LAZ file is basically the area north of the big curve in the road up to that northern road section.

I downloaded QGIS and did the best I could but it's so far over my head I can't have any confidence in my determination (which is: I can't see any evidence of man). I suppose it's possible the camp could have been a bit west of the LAZ file.

I was hoping someone with the knowledge and willingness might take a look at this and see if what they think.

https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/MT_Lincoln_D24/MT_Lincoln_1_D24/LAZ/USGS_LPC_MT_Lincoln_D24_150460.lazhttps://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/MT_Lincoln_D24/MT_Lincoln_1_D24/LAZ/USGS_LPC_MT_Lincoln_D24_150460.laz

Thank you!

Looking to sell a Hesai XT32M2X, previously purchased for a research project in 2023 for $7k. Was used for less than 10 hrs, basically brand new, will include the junction box and power adapter, will ship/located in US. $5.5k or best offer.

PM if interested, open to share more information and photos.

Hi all,

I'm working on a mine site survey project and I'm stuck trying to align two point clouds of the same tunnel system in CloudCompare, and hoping someone with more registration experience can point me in the right direction.

Setup:

• Both clouds were captured with a GeoSLAM ZEB Horizon (handheld SLAM LiDAR)
• One cloud appears to be in real-world coordinates (large box dimensions, offset consistent with a UTM-based global shift), the other is in a local/arbitrary coordinate frame with no georeferencing yet
• I'm trying to bring the second cloud into alignment with the first

What I've tried:

• Point-pair picking (Tools → Registration → Align) — picked 3-4 corresponding points I could identify in both clouds
• Followed up with Fine Registration (ICP)
• Getting a Final RMS around 2.5-2.6, but the resulting transformation matrix looks odd (large off-diagonal rotation values) and the clouds don't look properly aligned visually after applying it

Where I'm stuck:

• Not sure if my point-pair picks were accurate/well-distributed enough, or if I'm picking genuinely matching physical features
• Wondering if the two clouds might not have enough geometric overlap for ICP to converge properly
• Unsure whether I should be doing something different given one cloud is georeferenced and the other isn't (should I georeference the second cloud first via GCPs before trying to register them together, or is direct point-cloud-to-point-cloud registration the better approach here?)

Has anyone dealt with aligning tunnel/underground SLAM scans like this? Any tips on picking good corresponding points in tunnel geometry (which tends to be repetitive/low-feature in places), or general troubleshooting for when ICP RMS looks "okay" but the visual alignment still looks off?

Appreciate any help — happy to share screenshots of the point-pair picks or the resulting transform if useful.

Is there a trick to getting the Ethernet working on these? I bought two of them so far from an eBay seller. One had the end chopped off already and the second one intact. I’m wanting to use it for an autonomous robot competition and need to interface with a Jetson Orin Nano.

Following the wiring table in the owners manual:

Red and Gray wired together to 19V
Black and Gray/White wired together to ground
White, Yellow, and Purple pair sealed - not used
Blue, Blue / White - Ethernet RX pair
Orange, Orange/White - Ethernet TX pair

I’ve tried wiring strait (TX to TX), crossover (TX to RX), direct to a laptop, through a switch, even tried wiring to a magjack, there’s nothing. Laptop says “Cable disconnected” or “NO CARRIER”. I put a scope on to TX+ and watched over a 30 second startup period and there’s nothing. No pulses, no attempt at communication.

I figured the first one was a dud, but have the same issue with the second one. I emailed the eBay seller and they’ve sold around 200 of these and only a few have had Ethernet problems. The manual states this is regular 100mb-TX connection and not T1 automotive. It should “just work” shouldn’t it?

Tengo nubes de puntos de LAT, necesito obtener valores de medicion como distancia entre postes, cuerda y flechado de conductores. Que software me recomiendan para hacer esto? gratuitos y pagos. Hay algun tutorial que recomienden tambien para el mismo trabajo?

https://imgur.com/gallery/VrLLvBb https://imgur.com/gallery/vPasdEc

I'm working with a colourized point cloud captured using a sensor mounted on top of a car for a research project (we didn't capture it, as it comes from a previous research project done by a partner). As a large surface was covered and the sensor provided a particularly high density point cloud, it has been tiled to simplify working with it

Due to a change in the project objectives, it seems that a mesh would be a better option since that accuracy isn't needed and the main purpose of the model is being used for rendering images. For small point clouds, programs like cloud compare can be used for that, but in this case, it would be desirable to do this process tile by tile

Do you know if there's any mesh reconstruction tool from point clouds that takes into account the frontiers between tiles? It doesn't need to be part of cloudcompare, I have also access to recap and metashape. Open source tools can be also used in case any of them better fits this use case

Pics of the the juicy goodness inside a pandar 40p

• Loads models (OBJ, FBX, PLY, LAS/LAZ point clouds, even Gaussian splats) straight in the browser — nothing uploaded to a server
• Lets you switch to Annotate mode, click the exact spot on the mesh or point cloud, and write the finding right there
• Anchors comments in 3D space instead of a text description — clicking a comment in the list flies the camera right to it
• Exports/imports as JSON, so findings travel with the model instead of living in someone’s spreadsheet
• Also handles 2D images, for assets that don’t have a 3D scan yet

Why I’m posting this:

It’s very much a v1, built out of that “spreadsheet vs. reality” pain point rather than a big product roadmap. No NDT heatmap overlays, no compliance templates (API 510/570/653, DOSH), no PostgreSQL backend for team collaboration — all things I have in mind for later. What it does solve, even at v1, is the “where exactly was that finding” problem.

If you’ve done RBI, NDT, or asset integrity work and have thoughts on what would actually make this useful in the field — offline iPad use, tie-ins to Maximo/SAP, specific compliance formats — I’d like to hear it before building the wrong thing next.

Repo’s here, free to use: https://github.com/zawawiAI/Open3DInspection

Hey everyone,

I am mapping offices and they will be used to train humanoid robots in a simulator, I have mapped the offices with my iPhone and the point cloud looks 1:1. though the indtant i try to compute it it becomes a huge mess of polygons. Any good software recommendations? I need Poisson Mesh as far as I know, I have tried cloudcompare and might not fully understand it. If that’s the case any good tutorials out there?

This image is just to show point cloud data quality.

Raw Orbbec capture on the left: window glass drops to black, no returns. Completed depth on the right fills coherent flat geometry where the sensor saw nothing.

For point cloud workflows this matters because glass partitions and mirrored walls are exactly the surfaces that break ICP, floorplan extraction, and collision meshes.

Grabbed these from the comparison page of Robbyant, an embodied AI company under Ant Group. Their interactive viewer lets you spin actual point clouds of all methods against ground truth, so you can judge for yourself. Depth 2.0 weights are not released; only the Vision backbones underneath are open under Apache-2.0.

Underground mining is one of the most demanding environments for sensing systems. Methane, coal dust, darkness, water mist, and heavy dust create serious challenges for both safety and perception.
Traditional monitoring solutions still have significant limitations:
Hidden gas hazards – Methane can accumulate in blind corners and go undetected until it reaches explosive concentrations.
Limited visibility – Cameras are easily affected by dust, water mist, and poor lighting, leading to false alarms and missed detections.
Explosion risks – Non-explosion-proof electronics may generate sparks or excessive heat, becoming potential ignition sources.
Lack of real-time 3D awareness – Roof deformation, equipment displacement, and tunnel changes are difficult to detect before they become critical.
Why Intrinsic Safety Matters
Unlike traditional explosion-proof designs that contain an explosion after ignition, intrinsic safety prevents ignition from occurring in the first place by limiting electrical energy below the level capable of igniting methane or coal dust.
This significantly reduces the risk of ignition, even under fault conditions.
Bringing Reliable 3D Perception Underground
An intrinsically safe 3D LiDAR provides:
✅ 360° environmental perception with long-range detection
✅ Accurate 3D point cloud monitoring for tunnel deformation and structural changes
✅ Stable performance in dust, water mist, and low-light conditions
✅ Easy integration with ROS/ROS2 and intelligent mining platforms
Typical Applications
Methane monitoring and hazardous area mapping
Tunnel deformation and roof collapse warning
Collision avoidance for mining equipment
Conveyor and stockpile volume measurement
Autonomous inspection robots and unmanned mining vehicles
Post-incident 3D mapping for rescue operations
Reliable perception is the foundation of safer and smarter mining.

🌐 Introducing Open3DInspection: Closing the Digital Twin-to-Inspection Gap

We've invested billions in digital twins—detailed 3D models of pipelines, plants, and assets. But here's the problem: our inspection data still lives in spreadsheets and emails, disconnected from the 3D geometry it describes.

I built Open3DInspection to fix that.

**What it does:**

It's a browser-based annotation layer for digital twins. Click on your 3D model (point cloud, BIM, photogrammetry output) and anchor inspection findings directly to the geometry. Observations persist, export as JSON, and flow back into your asset management systems—turning static 3D models into living, inspection-informed digital twins.

**The digital twin loop:**

1. Load your digital twin (OBJ, FBX, LAS/LAZ, Gaussian splats)

2. Conduct inspection → drop pins with UT readings, corrosion notes, heat maps

3. Export annotations → integrate with SAP, Maximo, ArcGIS

4. Update the digital twin with real-world condition data

5. Use insights for predictive maintenance and asset lifecycle decisions

**Who needs this:**

• Oil & gas operators managing RBI-driven inspection programs on thousands of assets

• Plant engineers turning point clouds into condition-informed digital twins

• Infrastructure teams (bridges, power, water) integrating drone surveys with asset data

• Asset managers who want inspection data spatially anchored—not detached in databases

**Why it matters:**

A digital twin without inspection data is just a 3D model. With Open3DInspection, your digital twin becomes the single source of truth for asset condition, enabling:

✓ Spatial queries ("show me all high-risk corrosion zones")

✓ Compliance proof (API 510/570/653 audits with geometry-tied evidence)

✓ Predictive maintenance (condition trends anchored to specific assets)

✓ Cross-team collaboration (inspectors, engineers, planners see the same geometry + data)

Repo: github.com/zawawiAI/Open3DInspection