Learning Flow-Guided Registration for RGB-Event Semantic Segmentation
This work addresses the challenge of aligning asynchronous RGB and event camera data for semantic segmentation, which is crucial for robotics and autonomous systems, representing a novel paradigm shift rather than an incremental improvement.
The paper tackles the problem of RGB-Event semantic segmentation by addressing spatiotemporal and modal misalignment, recasting it from fusion to registration, and achieves state-of-the-art results validated on four large-scale datasets.
Event cameras capture microsecond-level motion cues that complement RGB sensors. However, the prevailing paradigm of treating RGB-Event perception as a fusion problem is ill-posed, as it ignores the intrinsic (i) Spatiotemporal and (ii) Modal Misalignment, unlike other RGB-X sensing domains. To tackle these limitations, we recast RGB-Event segmentation from fusion to registration. We propose BRENet, a novel flow-guided bidirectional framework that adaptively matches correspondence between the asymmetric modalities. Specifically, it leverages temporally aligned optical flows as a coarse-grained guide, along with fine-grained event temporal features, to generate precise forward and backward pixel pairings for registration. This pairing mechanism converts the inherent motion lag into terms governed by flow estimation error, bridging modality gaps. Moreover, we introduce Motion-Enhanced Event Tensor (MET), a new representation that transforms sparse event streams into a dense, temporally coherent form. Extensive experiments on four large-scale datasets validate our approach, establishing flow-guided registration as a promising direction for RGB-Event segmentation. Our code is available at: https://github.com/zyaocoder/BRENet.