# PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds Source: [PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:06:19.934Z Authors: Sukai Wang, Yuxiang Sun, Chengju Liu, Ming Liu Published: 2020-02-26T15:19:28.000Z ## Abstract Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds. Most traditional tracking approaches use filters (e.g., Kalman filter or particle filter) to predict object locations in a time sequence, however, they are vulnerable to extreme motion conditions, such as sudden braking and turning. In this letter, we propose PointTrackNet, an end-to-end 3-D object detection and tracking network, to generate foreground masks, 3-D bounding boxes, and point-wise tracking association displacements for each detected object. The network merely takes as input two adjacent point-cloud frames. Experimental results on the KITTI tracking dataset show competitive results over the state-of-the-arts, especially in the irregularly and rapidly changing scenarios. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()