Neural Implicit Event Generator for Motion Tracking
This work addresses motion tracking for mobile robotics applications with limited computational resources, though it is incremental as it builds on implicit neural representations for event-based vision.
The paper tackles motion tracking from sparse event data by introducing an implicit event generator (IEG) that updates state estimates based on differences between observed and generated events, enabling efficient tracking suitable for mobile robotics. It was validated on AR marker tracking, showing robust performance in noisy real-world environments.
We present a novel framework of motion tracking from event data using implicit expression. Our framework use pre-trained event generation MLP named implicit event generator (IEG) and does motion tracking by updating its state (position and velocity) based on the difference between the observed event and generated event from the current state estimate. The difference is computed implicitly by the IEG. Unlike the conventional explicit approach, which requires dense computation to evaluate the difference, our implicit approach realizes efficient state update directly from sparse event data. Our sparse algorithm is especially suitable for mobile robotics applications where computational resources and battery life are limited. To verify the effectiveness of our method on real-world data, we applied it to the AR marker tracking application. We have confirmed that our framework works well in real-world environments in the presence of noise and background clutter.