mlsanityData to 14 Oct 2026

3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation

arXiv 2110.02531 ↗First posted 6 Oct 2021cs.CVcited by 1

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Abstract (arXiv)

3D object detection using LiDAR data remains a key task for applications like autonomous driving and robotics. Unlike in the case of 2D images, LiDAR data is almost always collected over a period of time. However, most work in this area has focused on performing detection independent of the temporal domain. In this paper we present 3D-FCT, a Siamese network architecture that utilizes temporal information to simultaneously perform the related tasks of 3D object detection and tracking. The network is trained to predict the movement of an object based on the correlation features of extracted keypoints across time. Calculating correlation across keypoints only allows for real-time object detection. We further extend the multi-task objective to include a tracking regression loss. Finally, we produce high accuracy detections by linking short-term object tracklets into long term tracks based on the predicted tracks. Our proposed method is evaluated on the KITTI tracking dataset where it is shown to provide an improvement of 5.57% mAP over a state-of-the-art approach.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 40 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

33 of 40 references.

References · 33

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21 more
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Minuet: Accelerating 3D Sparse Convolutions on GPUscited by 2