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Recent development of 3D sensors allows the acquisition of extremely dense 3Dpoint clouds of large-scale scenes. The main challenge of processing such largepoint clouds remains in the size of the data, which induce expensivecomputational and memory cost. In this context, the full resolution cloud isparticularly 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 deeplearning networks. In this paper, we introduce a new generic deep learningpipeline to exploit the full precision of large scale point clouds, but onlyfor objects that require details. The core idea of our approach is to split upthe process into multiple sub-networks which operate on different resolutionsand with each their specific classes to retrieve. Thus, the pipeline allowseach class to benefit either from noise and memory cost reduction of asub-sampling or from fine-grained details.

Multi-resolution deep learning pipeline for dense large scale point clouds