  Neuromorphic vision is a bio-inspired technology that has triggered a
paradigm shift in the computer-vision community and is serving as a key-enabler
for a multitude of applications. This technology has offered significant
advantages including reduced power consumption, reduced processing needs, and
communication speed-ups. However, neuromorphic cameras suffer from significant
amounts of measurement noise. This noise deteriorates the performance of
neuromorphic event-based perception and navigation algorithms. In this paper,
we propose a novel noise filtration algorithm to eliminate events which do not
represent real log-intensity variations in the observed scene. We employ a
Graph Neural Network (GNN)-driven transformer algorithm, called
GNN-Transformer, to classify every active event pixel in the raw stream into
real-log intensity variation or noise. Within the GNN, a message-passing
framework, called EventConv, is carried out to reflect the spatiotemporal
correlation among the events, while preserving their asynchronous nature. We
also introduce the Known-object Ground-Truth Labeling (KoGTL) approach for
generating approximate ground truth labels of event streams under various
illumination conditions. KoGTL is used to generate labeled datasets, from
experiments recorded in chalenging lighting conditions. These datasets are used
to train and extensively test our proposed algorithm. When tested on unseen
datasets, the proposed algorithm outperforms existing methods by 8.8% in terms
of filtration accuracy. Additional tests are also conducted on publicly
available datasets to demonstrate the generalization capabilities of the
proposed algorithm in the presence of illumination variations and different
motion dynamics. Compared to existing solutions, qualitative results verified
the superior capability of the proposed algorithm to eliminate noise while
preserving meaningful scene events.
