ExPO-Shape: A Benchmark and Event-Guided Novel View Synthesis for Exposure-Degraded Object Pose Estimation

Estimating single-view pose under exposure degradation requires both semantic appearance priors and illumination-robust geometric cues, neither of which can be provided by RGB or events alone. The current bottleneck of RGB–event pose estimators lies mainly in the lack of large-scale exposure-degraded benchmarks for unseen-object generalization and in the reliance on object 3D models. To fill this gap, we build ExPO-Shape, a large-scale Exposure-degraded object POse benchmark comprising 48,145 paired RGB–event samples of 9,629 unique objects across 19 categories, rendered under randomly sampled under- and over-exposure conditions. To further facilitate research on model-free, generalizable RGB-event pose estimation, we propose EVENPose, an event-guided single-view framework for robust pose estimation under exposure shifts. EVENPose stabilizes RGB embeddings with event cues instead of depth priors, guides novel-view synthesis with event-derived structure, and enforces event-level consistency to improve structural fidelity and generalization. Extensive experiments demonstrate that EVENPose serves as a strong baseline on ExPO-Shape and transfers competitively to existing RGB–event benchmarks. Overall, ExPO-Shape serves as a scalable training benchmark for model-free, generalizable RGB-event pose estimation and enables systematic evaluation of single-view robustness to exposure degradation.