论文标题
点：图像增强点云
PointSee: Image Enhances Point Cloud
论文作者
论文摘要
融合3D对象检测（3OD）的多模式信息的趋势。但是，在设计多模式融合纽托克时，低轻质，轻巧的灵活性和功能不准确的挑战性问题仍然没有很好地解决。我们提出了PointSee，这是一种轻巧，灵活且有效的多模式融合解决方案，以通过语义特征增强与场景图像组装的LIDAR点云的增强来促进各种3OD网络。除了现有的3OD智慧之外，PointSee还包括一个隐藏的模块（HM）和一个可见的模块（SM）：HM装饰以离线融合方式使用2D图像信息来装饰LiDAR Point Clouds，从而导致现有3OD网络的最小化甚至不适应； SM通过获取点的代表性语义特征，进一步丰富了LiDAR点云，从而增强了现有3OD网络的性能。除了Pointsee的新体系结构外，我们还提出了一种简单而有效的培训策略，以减轻2D对象检测网络的潜在不准确回归。对流行的室外/室内基准测试的广泛实验表明，我们对二十二个最先进的对方的数值改进。
There is a trend to fuse multi-modal information for 3D object detection (3OD). However, the challenging problems of low lightweightness, poor flexibility of plug-and-play, and inaccurate alignment of features are still not well-solved, when designing multi-modal fusion newtorks. We propose PointSee, a lightweight, flexible and effective multi-modal fusion solution to facilitate various 3OD networks by semantic feature enhancement of LiDAR point clouds assembled with scene images. Beyond the existing wisdom of 3OD, PointSee consists of a hidden module (HM) and a seen module (SM): HM decorates LiDAR point clouds using 2D image information in an offline fusion manner, leading to minimal or even no adaptations of existing 3OD networks; SM further enriches the LiDAR point clouds by acquiring point-wise representative semantic features, leading to enhanced performance of existing 3OD networks. Besides the new architecture of PointSee, we propose a simple yet efficient training strategy, to ease the potential inaccurate regressions of 2D object detection networks. Extensive experiments on the popular outdoor/indoor benchmarks show numerical improvements of our PointSee over twenty-two state-of-the-arts.
