An LSTM Approach to Temporal

3D Object Detection in LiDAR Point Clouds


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, we propose a sparse LSTM-based multi-frame 3d object detection algorithm to pass explicit memory states associated with 3d locations across frames. We use a U-Net style 3D sparse convolution network to extract features for each frame’s LiDAR point-cloud. These features are fed to the LSTM module together with the hidden and memory features from the last frame to predict the 3d objects in the current frame as well as hidden and memory features that are passed to the next frame. Experiments on the Waymo Open Dataset show that our algorithm outperforms the traditional frame by frame approach by 7.5% mAP@0.7 and other multi-frame approaches by 1.2% while using less memory and computation per frame. To the best of our knowledge, this is the first work to use an LSTM for 3D object detection in sparse point clouds.



title={An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds},

author={Huang, Rui and Zhang, Wanyue and Kundu, Abhijit and Pantofaru, Caroline and Ross, David A and Funkhouser, Thomas and Fathi, Alireza},

booktitle={European Conference on Computer Vision},