EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection

• URL: http://arxiv.org/abs/2007.08856v1
• Date: Fri, 17 Jul 2020 09:33:05 GMT
• Title: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection
• Authors: Tengteng Huang, Zhe Liu, Xiwu Chen and Xiang Bai
• Abstract summary: We aim at addressing two critical issues in the 3D detection task, including the exploitation of multiple sensors. We propose a novel fusion module to enhance the point features with semantic image features in a point-wise manner without any image annotations. We design an end-to-end learnable framework named EPNet to integrate these two components.
• Score: 60.097873683615695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we aim at addressing two critical issues in the 3D detection task, including the exploitation of multiple sensors~(namely LiDAR point cloud and camera image), as well as the inconsistency between the localization and classification confidence. To this end, we propose a novel fusion module to enhance the point features with semantic image features in a point-wise manner without any image annotations. Besides, a consistency enforcing loss is employed to explicitly encourage the consistency of both the localization and classification confidence. We design an end-to-end learnable framework named EPNet to integrate these two components. Extensive experiments on the KITTI and SUN-RGBD datasets demonstrate the superiority of EPNet over the state-of-the-art methods. Codes and models are available at: \url{https://github.com/happinesslz/EPNet}.

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