ConTraPosE: Geometry-Aware Contrast Maximization for Event-Based Object Tracking

Accurate 6DoF object tracking under rapid motion remains challenging for frame-based cameras due to motion blur and limited temporal resolution. Event cameras alleviate these limitations, but their sparse asynchronous measurements provide little direct appearance information, making conventional appearance-based pose estimation difficult. We introduce ConTraPosE, a tracking method based on a geometry-aware contrast formulation for estimating 6DoF object motion directly from raw events. Given a known CAD model and a candidate pose trajectory, we use the object geometry to construct its induced image-plane motion field, warp the observed events accordingly, and evaluate the resulting alignment with a normalized contrast objective. This provides a direct pose-update signal without image reconstruction or explicit event-to-object correspondences. We integrate this objective into continuous event-only tracking with adaptive temporal windows and optionally incorporate depth as an additional geometric constraint for absolute pose correction. Experiments on Event6D and SPADES show strong performance in both event-only and event–depth settings, with consistent robustness across different object geometries, appearances, motion regimes, event corruption, occlusion, and initialization perturbations. These results demonstrate that geometry-induced motion consistency provides an effective alternative to appearance-based comparison for event-driven 6DoF object tracking.