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Detecting dynamic objects and predicting static road information such asdrivable areas and ground heights are crucial for safe autonomous driving.Previous works studied each perception task separately, and lacked a collectivequantitative analysis. In this work, we show that it is possible to perform allperception tasks via a simple and efficient multi-task network. Our proposednetwork, LidarMTL, takes raw LiDAR point cloud as inputs, and predicts sixperception outputs for 3D object detection and road understanding. The networkis based on an encoder-decoder architecture with 3D sparse convolution anddeconvolution operations. Extensive experiments verify the proposed method withcompetitive accuracies compared to state-of-the-art object detectors and othertask-specific networks. LidarMTL is also leveraged for online localization.Code and pre-trained model have been made available athttps://github.com/frankfengdi/LidarMTL.

A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding