The paper presents a Point-Trajectory Transformer (PTT) for efficient temporal 3D object detection. The key insights are:
Leveraging multi-frame point clouds can lead to memory overhead, while considering multi-frame proposal trajectories can be efficient and effective.
PTT efficiently establishes connections between single-frame point clouds and multi-frame proposals, facilitating the utilization of rich LiDAR data with reduced memory overhead.
PTT employs long-term, short-term, and future-aware encoders to enhance feature learning over temporal information, and a point-trajectory aggregator to integrate point clouds and proposals effectively.
Experiments on the Waymo Open Dataset show that PTT performs favorably against state-of-the-art approaches, using more frames but with smaller memory overhead and faster runtime.
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ข้อมูลเชิงลึกที่สำคัญจาก
by Kuan-Chih Hu... ที่ arxiv.org 04-25-2024
https://arxiv.org/pdf/2312.08371.pdfสอบถามเพิ่มเติม