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• W3204445544 abstract "Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory. In this paper, we dig into the 3D object detection task and reformulate it as the sub-tasks of object localization and appearance perception, which benefits to a deep excavation of reciprocal information underlying the entire task. We introduce a Dynamic Feature Reflecting Network, named DFR-Net, which contains two novel standalone modules: (i) the Appearance-Localization Feature Reflecting module (ALFR) that first separates task-specific features and then self-mutually reflects the reciprocal features; (ii) the Dynamic Intra-Trading module (DIT) that adaptively realigns the training processes of various sub-tasks via a self-learning manner. Extensive experiments on the challenging KITTI dataset demonstrate the effectiveness and generalization of DFR-Net. We rank 1 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>st</sup> among all the monocular 3D object detectors in the KITTI test set (till March 16th, 2021). The proposed method is also easy to be plug-and-play in many cutting-edge 3D detection frameworks at negligible cost to boost performance. The code will be made publicly available." @default.
• W3204445544 created "2021-10-11" @default.
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• W3204445544 date "2021-10-01" @default.
• W3204445544 modified "2023-09-24" @default.
• W3204445544 title "The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection" @default.
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• W3204445544 doi "https://doi.org/10.1109/iccv48922.2021.00271" @default.
• W3204445544 hasPublicationYear "2021" @default.
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