  Current neural networks-based object detection approaches processing LiDAR
point clouds are generally trained from one kind of LiDAR sensors. However,
their performances decrease when they are tested with data coming from a
different LiDAR sensor than the one used for training, i.e., with a different
point cloud resolution. In this paper, R-AGNO-RPN, a region proposal network
built on fusion of 3D point clouds and RGB images is proposed for 3D object
detection regardless of point cloud resolution. As our approach is designed to
be also applied on low point cloud resolutions, the proposed method focuses on
object localization instead of estimating refined boxes on reduced data. The
resilience to low-resolution point cloud is obtained through image features
accurately mapped to Bird's Eye View and a specific data augmentation procedure
that improves the contribution of the RGB images. To show the proposed
network's ability to deal with different point clouds resolutions, experiments
are conducted on both data coming from the KITTI 3D Object Detection and the
nuScenes datasets. In addition, to assess its performances, our method is
compared to PointPillars, a well-known 3D detection network. Experimental
results show that even on point cloud data reduced by $80\%$ of its original
points, our method is still able to deliver relevant proposals localization.
