arXiv:2605.00271v3 Announce Type: replace-cross Abstract: Event cameras provide several unique advantages over standard frame-based sensors, including high temporal resolution, low latency, and robustness to extreme lighting. However, existing learning-based approaches for event processing are typically confined to narrow, task-specific silos and lack the ability to generalize across modalities. We address this gap with REALM, a cross-modal framework that learns an RGB- and Event-Aligned Latent Manifold by projecting event representations into the pretrained latent space of RGB foundation mode
The proliferation of various sensor modalities and the increasing demand for robust real-world AI applications are driving the need for unified cross-modal perception systems.
This development is crucial for advancing AI's ability to interpret complex environments by merging high-temporal-resolution event data with rich RGB visual information, overcoming limitations of single-modality systems.
AI systems will gain improved perception in challenging lighting conditions and with dynamic scenes, leading to more robust performance in applications like robotics and autonomous systems.
- · Robotics companies
- · Autonomous vehicle developers
- · AI research labs
- · Event camera manufacturers
- · Developers focused solely on single-modality vision systems
Improved performance of AI systems in real-world environments requiring high-speed sensing and robust vision.
Accelerated development and deployment of advanced robotic systems and autonomous devices capable of more sophisticated interactions with their surroundings.
Enhanced AI capability leading to new applications in areas like human-robot collaboration and difficult industrial environments.
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Read at arXiv cs.AI