论文标题
通过以对象为中心的图像融合来增强3D对象检测
Boosting 3D Object Detection via Object-Focused Image Fusion
论文作者
论文摘要
3D对象检测通过将点云作为唯一的输入来取得了显着的进展。但是，点云通常会遭受不完整的几何结构和缺乏语义信息，这使检测器难以准确地对检测到的对象进行分类。在这项工作中，我们专注于如何有效利用来自图像的对象级信息来提高基于点的3D检测器的性能。我们提出DEMF，这是一种简单而有效的方法，将图像信息融合到点特征中。给定一组点特征和图像特征图，DEMF通过将3D点的投影2D位置作为参考来自适应地汇总图像特征。我们在挑战性的Sun RGB-D数据集上评估了我们的方法，从而提高了最新结果（+2.1 map@[email protected]和[email protected]）。代码可在https://github.com/haoy945/demf上找到。
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 [email protected] and [email protected]). Code is available at https://github.com/haoy945/DeMF.
