🗓️ June 9-11
#Shoptalk Barcelona is our next stop. Meet Yiannis Darya to see how ALLSIDES creates true-to-life 3D digital twins.
Let’s talk: richer product pages, better visualization, fewer returns.
#digitaltwins#3Ddata#3Dscanning#RetailTech
Harbor LiDAR data output.
Clear vessel geometry, dock layout, and spatial relationships — ready for mapping, inspection, and digital port management.
#LiDAR#PortMapping#PointCloud#3DData
Streamline 3D data selection with PV-SAM! Our #ParaView & LidarView plugin uses @Meta Segment Anything Model to make interactive segmentation faster, precise, and more intuitive, perfect for #LiDAR, medical imaging, and industrial inspection.
Read more: ow.ly/pv3Y50XGhWp#3DData#ParaViewPlugin#TechInnovation
Parking lots are actually harder than highways for autonomous vehicles.
No joke.
Highways are clean and predictable.
Parking lots are chaos in a box.
If an AV can’t handle parking lots, it can’t handle reality.
#ROVRNetwork#AutonomousVehicles#AI#3DData
City nights are messy! Think glare, shadows, headlights, reflections, motion everywhere. 🌃🚗✨
That’s why we built the TarantulaX to shine in low-light environments.
It captures clean data and accurate depth even under the most chaotic night traffic.
Real autonomy needs real-world conditions.
The TX handles them.
#ROVRNetwork#TarantulaX#NightData#3DData#AI
Sensor fusion sounds complicated, but here’s the simple version:
No single sensor sees the full truth.
Cameras see color texture.
LiDAR sees shape distance.
GNSS IMU see position motion.
Sensor fusion combines them into one accurate view of the world! The backbone of a reliable world model.
This is the stack ROVR is collecting data for.
#ROVRNetwork#SensorFusion#WorldModel#3DData
Most autonomous driving datasets come from “perfect conditions.” But the real world isn’t perfect — and that’s why non-standard vehicle data is critical. 🚗⚡
Driving data from unusual setups, unique vehicle types, different heights, and diverse sensor placements helps AV models learn to handle:
🔹 different blind spots
🔹 unusual reflections shadows
🔹 varied camera angles
🔹 non-ideal sensor mounting
🔹 real-world imperfections
The world isn’t standardized
Vehicles aren’t standardized
Training data shouldn’t be either!
Non-standard data makes autonomous systems safer, more adaptable, and more prepared for what can happen on real roads. 🌍
#ROVRNetwork#WorldModel#AutonomousVehicles#AI#SensorFusion#3DData
Seasonal data is essential and fall brings challenges the LightCone must learn from. 🍁🌐
When leaves start covering the ground, everything changes:
🔸 road markings get hidden
🔸 textures become uneven
🔸 traction zones shift
🔸 colors blend together
🔸 shadows become more complex
These conditions push perception systems harder than almost any other season.
Capturing data through fall helps the LightCone understand how roads look when they’re covered in leaves, layered in color, and constantly changing.
Every season teaches the world model something new 🍂✨
#ROVRNetwork#LightCone#LiDAR#3DData#WorldModel#AutonomousVehicles#AI#Robotics
School buses are one of the most important, and most challenging, vehicles for autonomous systems to understand. 🚌⚠️
They have:
🔸 unique shapes sizes
🔸 different stopping patterns
🔸 flashing lights
🔸 extended stop signs
🔸 children crossing unpredictably
🔸 strict legal right-of-way rules
Most AV datasets barely include them, but they’re everywhere in the real world! 🌍
Collecting school bus data helps world models learn how to:
✔ identify buses from all angles
✔ react correctly to stop signals
✔ predict loading/unloading behavior
✔ recognize children near the road
✔ follow safety laws that vary by state
If AVs are going to be safe for everyone, they must understand school buses better!
#ROVRNetwork#AutonomousVehicles#AI#WorldModel#Safety#3DData