Recent development of 3D sensors allows the acquisition of extremely dense 3D point clouds of large-scale scenes. The main challenge of processing such large point clouds remains in the size of the data, which induce expensive computational and memory cost. In this context, the full resolution cloud is particularly hard to process, and details it brings are rarely exploited. Although fine-grained details are important for detection of small objects, they can alter the local geometry of large structural parts and mislead deep learning networks. In this paper, we introduce a new generic deep learning pipeline to exploit the full precision of large scale point clouds, but only for objects that require details. The core idea of our approach is to split up the process into multiple sub-networks which operate on different resolutions and with each their specific classes to retrieve. Thus, the pipeline allows each class to benefit either from noise and memory cost reduction of a sub-sampling or from fine-grained details.
翻译:最近开发的三维传感器可以获取大片场景极稠密的三维点云云。处理这些大点云的主要挑战仍然是数据大小,这会引起昂贵的计算和记忆成本。在这方面,整块分辨率云特别难以处理,其带来的细节很少被利用。尽管细微的细微细节对于探测小物体很重要,但它们可以改变大结构部分的局部几何和误导深层学习网络。在本文中,我们引入了新的通用深层次学习管道,以利用大点云的完全精确度,但只适用于需要细节的物体。我们的方法的核心思想是将这一过程分成多个子网络,这些子网络以不同分辨率运行,每个特定的类别都可检索。因此,管道使每个类别都能从子抽样的噪音和记忆成本减少或精细细的细细细细节中获益。