SPAR: Monocular Sparse 3D Point Tracking with Events

Continuous 3D point tracking is fundamental to dynamic scene understanding. Monocular event streams provide sparse and intermittent image-plane observations, making it challenging to maintain query identity and recover metric motion without direct longitudinal measurements. We propose SPAR, a sparse 3D point tracker that uses only monocular events after image-based initialization. Its query-conditioned interval–geometric matching combines observed-event support with sampled pre-trigger response history through Hit–Survival and refines correspondences with reliability-weighted local motion. An interval-evidence marginal objective further incorporates this candidate-conditioned history into correspondence learning. For 3D inference, we introduce a Query-conditioned Anchor-Relative Trajectory head (QART) that separates one-time metric anchoring from event-driven relative motion and carries correspondence confidence, support reliability, and local geometry into temporal 3D inference. We construct EventTraj3D from BlinkVision, comprising 13.630 billion asynchronous events and 6,494 annotated query trajectories. Experiments show lower 2D and 3D errors on EventTraj3D and the lowest RMSE among compared methods on E-3DTrack, while initialization perturbations show reduced sensitivity to depth-prior errors.