EventGeoPose: Pose-Referenced Event Encoding and Event-Geometry Consistency for Spiking 6D Pose Estimation

Event cameras have shown considerable potential for 6D object pose estimation under fast motion and challenging illumination. However, current event-based approaches still rely heavily on dense ANN computation, fixed-duration temporal discretization, and conventional pose supervision, which respectively limit energy-efficient event processing, representation of highly nonuniform event dynamics, and explicit exploitation of event-derived geometric evidence. To address these issues, we present EventGeoPose, a directly trained SNN framework for 6D object pose estimation from event streams. We construct the spiking backbone with I-LIF neurons to improve the numerical fidelity of continuous regression while retaining spike-driven inference. We propose Pose-Referenced Adaptive Event Encoding, which combines activity-adaptive temporal discretization with duration-normalized local event-rate encoding to mitigate motion-state mixing and uneven observation density. We further introduce Event-Geometry Consistency Regularization, which couples predicted 3D object geometry with observed event structures through differentiable image-space reprojection, providing observation-aware geometric supervision for pose prediction. Additionally, we construct two complementary event-based datasets, EVA-6D-Syn and EVA-6D-Real, covering diverse motion, illumination, and real-sensor conditions. Extensive experiments demonstrate consistent improvements over representative methods while maintaining low theoretical inference energy, highlighting the potential of event-driven SNNs for robust and energy-efficient 6D pose estimation.