Multi-Task Multi-Sensor Fusion for 3D Object Detection

CVPR 2019  ·  Ming Liang, Bin Yang, Yun Chen, Rui Hu, Raquel Urtasun ·

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. Our experiments show that all these tasks are complementary and help the network learn better representations by fusing information at various levels. Importantly, our approach leads the KITTI benchmark on 2D, 3D and BEV object detection, while being real time.

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Object Detection KITTI Cars Easy UberATG-MMF AP 86.81% # 12
3D Object Detection KITTI Cars Hard UberATG-MMF AP 68.41% # 15
3D Object Detection KITTI Cars Moderate UberATG-MMF AP 76.75% # 17

Methods


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