# A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding Source: [A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:00:47.140Z Authors: Di Feng, Yiyang Zhou, Chenfeng Xu, Masayoshi Tomizuka, Wei Zhan Published: 2021-03-06T08:00:26.000Z ## Abstract Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving. Previous works studied each perception task separately, and lacked a collective quantitative analysis. In this work, we show that it is possible to perform all perception tasks via a simple and efficient multi-task network. Our proposed network, LidarMTL, takes raw LiDAR point cloud as inputs, and predicts six perception outputs for 3D object detection and road understanding. The network is based on an encoder-decoder architecture with 3D sparse convolution and deconvolution operations. Extensive experiments verify the proposed method with competitive accuracies compared to state-of-the-art object detectors and other task-specific networks. LidarMTL is also leveraged for online localization. Code and pre-trained model have been made available at https://github.com/frankfengdi/LidarMTL. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()