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Boosting 3D Object Detection Via Object-Focused Image Fusion.

CoRR(2022)

Cited 12|Views30
Abstract
3D object detection has achieved remarkable progress by taking point clouds as the only input. However, point clouds often suffer from incomplete geometric structures and the lack of semantic information, which makes detectors hard to accurately classify detected objects. In this work, we focus on how to effectively utilize object-level information from images to boost the performance of point-based 3D detector. We present DeMF, a simple yet effective method to fuse image information into point features. Given a set of point features and image feature maps, DeMF adaptively aggregates image features by taking the projected 2D location of the 3D point as reference. We evaluate our method on the challenging SUN RGB-D dataset, improving state-of-the-art results by a large margin (+2.1 mAP@0.25 and +2.3mAP@0.5). Code is available at https://github.com/haoy945/DeMF.
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Object Detection,Interest Point Detectors,Object Recognition,Feature Matching,Salient Object Detection
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要点】:该论文提出了一种名为DeMF的方法,通过以2D图像点为参考,自适应地聚合图像特征,有效地将图像信息融合到点特征中,显著提高了基于点的3D检测器的性能。

方法】:DeMF方法通过考虑3D点的2D投影位置,自适应地融合图像特征。

实验】:在具有挑战性的SUN RGB-D数据集上进行评估,结果显示,该方法大幅提升了检测性能,相比现有最佳方法,平均精度(mAP)在0.25阈值下提高了2.1点,在0.5阈值下提高了2.3点。相关代码可在GitHub上获取。