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

R-AGNO-RPN: A LIDAR-Camera Region Deep Network for Resolution-Agnostic Detection