HDNET: Exploiting HD Maps for 3D Object Detection

• URL: http://arxiv.org/abs/2012.11704v1
• Date: Mon, 21 Dec 2020 21:59:54 GMT
• Title: HDNET: Exploiting HD Maps for 3D Object Detection
• Authors: Bin Yang, Ming Liang, Raquel Urtasun
• Abstract summary: We show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. We design a single stage detector that extracts geometric and semantic features from the HD maps. As maps might not be available everywhere, we also propose a map prediction module that estimates the map on the fly from raw LiDAR data.
• Score: 99.49035895393934
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper we show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. Towards this goal, we design a single stage detector that extracts geometric and semantic features from the HD maps. As maps might not be available everywhere, we also propose a map prediction module that estimates the map on the fly from raw LiDAR data. We conduct extensive experiments on KITTI as well as a large-scale 3D detection benchmark containing 1 million frames, and show that the proposed map-aware detector consistently outperforms the state-of-the-art in both mapped and un-mapped scenarios. Importantly the whole framework runs at 20 frames per second.

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