  Learned pointcloud representations do not generalize well with an increase in
distance to the sensor. For example, at a range greater than 60 meters, the
sparsity of lidar pointclouds reaches to a point where even humans cannot
discern object shapes from each other. However, this distance should not be
considered very far for fast-moving vehicles: A vehicle can traverse 60 meters
under two seconds while moving at 70 mph. For safe and robust driving
automation, acute 3D object detection at these ranges is indispensable. Against
this backdrop, we introduce faraway-frustum: a novel fusion strategy for
detecting faraway objects. The main strategy is to depend solely on the 2D
vision for recognizing object class, as object shape does not change
drastically with an increase in depth, and use pointcloud data for object
localization in the 3D space for faraway objects. For closer objects, we use
learned pointcloud representations instead, following state-of-the-art. This
strategy alleviates the main shortcoming of object detection with learned
pointcloud representations. Experiments on the KITTI dataset demonstrate that
our method outperforms state-of-the-art by a considerable margin for faraway
object detection in bird's-eye-view and 3D. Our code is open-source and
publicly available: https://github.com/dongfang-steven-yang/faraway-frustum.
