Event-LiDAR: 3D Eventification for Efficient Point Cloud Processing

We propose Event-LiDAR* , a 3D eventification frameworkthat converts conventional LiDAR scans into temporally sparse repre-sentations for efficient point cloud processing. Unlike dense multi-scanprocessing, naive frame differencing, or correspondence-based residuals,3D event extraction is formulated as a temporal estimation problemunder sparse, viewpoint-dependent observations. Short-term geometricevolution across consecutive scans is approximated with a first-ordergeometric model, and deviations unexplained by this model are treatedas events. This yields compact, information-preserving representationsthat suppress redundancy while retaining changes unpredictable by thefirst-order model. Event-LiDAR is applied to LiDAR-based 3D objectdetection with existing backbones, and an event-aware network designis further introduced to reallocate modeling capacity toward the inputstage for sparse inputs. On nuScenes, under the 1F+9T setting witha 72% point reduction, Event-LiDAR maintains accuracy comparableto full-scan baselines while achieving 23% faster end-to-end inferenceon PTv3, driven in part by a 32% speedup in its feature extractionbackbone. The gains generalize across backbones, with a 9% end-to-endspeedup on CenterPoint under the same setting. Training is likewiseaccelerated by 17% under the same setting. Event-LiDAR thus serves asa low-latency, architecture-agnostic geometric preprocessor inspired byevent-based sensing, providing a practical front-end for efficient 3D pointcloud perception.