  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.
