VADet: Multi-Frame LiDAR 3D Object Detection using Variable Aggregation

Chengjie Huang, Vahdat Abdelzad, Sean Sedwards, Krzysztof Czarnecki; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 711-720

Input aggregation is a simple technique used by state-of-the-art LiDAR 3D object detectors to improve detection. However increasing aggregation is known to have diminishing returns and even performance degradation due to objects responding differently to the number of aggregated frames. To address this limitation we propose an efficient adaptive method which we call Variable Aggregation Detection (VADet). Instead of aggregating the entire scene using a fixed number of frames VADet performs aggregation per object with the number of frames determined by an object's observed properties such as speed and point density. VADet thus reduces the inherent trade-offs of fixed aggregation and is not architecture specific. To demonstrate its benefits we apply VADet to three popular single-stage detectors and achieve state-of-the-art performance on the Waymo dataset.