The technology behind our point cloud recognition engine.
The core of NebulaRecognition® is a point cloud recognition engine that can learn from single point cloud sample of an object and locate that point cloud anywhere in the scene regardless to its location (X, Y, Z) and orientation (Rx, Ry, Rz). The learning and recognition engine can run on low power CPU and run fast. This goes against the grain of libraries from academia or open source that require powerful GPU and CPU.
Point cloud libraries use segmentation techniques — feature-engineering as front-end, or deep neural network (DNN) — then a variant of iterative closest point is deployed. Success of segmentation is critical and requires “hand crafting” of what segmentation algorithm needs to be used. Every class of objects requires a different segmentation algorithm, which makes them all an engineered solution — scalability is out the window.
Unlike the competition, there is no bag of tricks. With RRI technology, all that a user needs to do is to push the teach button, erase unwanted background and recognize the object.