https://arxiv.org/abs/2108.10312 Exploring Simple 3D Multi-Object Tracking for Autonomous Driving 3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline and inevitably require a heuristic matching step for the detection association. In arxiv.org 이번에 review할 논문은 3D MOT 논문 중 Joint ..
https://arxiv.org/abs/2309.06036 Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar? Online 3D multi-object tracking (MOT) has recently received significant research interests due to the expanding demand of 3D perception in advanced driver assistance systems (ADAS) and autonomous driving (AD). Among the existing 3D MOT framewor..
https://arxiv.org/abs/1909.12605 Towards Real-Time Multi-Object Tracking Modern multiple object tracking (MOT) systems usually follow the \emph{tracking-by-detection} paradigm. It has 1) a detection model for target localization and 2) an appearance embedding model for data association. Having the two models separately executed arxiv.org Introduction 현대의 MOT system은 주로 tracking-by-detection 방식을 ..
https://arxiv.org/abs/1711.06396 VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly sparse LiDAR point cloud with a region proposal network (RP arxiv.org 1. Point cloud의 '어떤 특성'때문에..
