An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds

@inproceedings{Huang2020AnLA,
  title={An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds},
  author={Rui Huang and Wanyue Zhang and Abhijit Kundu and C. Pantofaru and David A. Ross and T. Funkhouser and A. Fathi},
  booktitle={ECCV},
  year={2020}
}
Detecting objects in 3D LiDAR data is a core technology for autonomous driving and other robotics applications. Although LiDAR data is acquired over time, most of the 3D object detection algorithms propose object bounding boxes independently for each frame and neglect the useful information available in the temporal domain. To address this problem, in this paper we propose a sparse LSTM-based multi-frame 3d object detection algorithm. We use a U-Net style 3D sparse convolution network to… Expand
Temp-Frustum Net: 3D Object Detection with Temporal Fusion
3D-MAN: 3D Multi-frame Attention Network for Object Detection
Offboard 3D Object Detection from Point Cloud Sequences

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