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Learned pointcloud representations do not generalize well with an increase indistance to the sensor. For example, at a range greater than 60 meters, thesparsity of lidar pointclouds reaches to a point where even humans cannotdiscern object shapes from each other. However, this distance should not beconsidered very far for fast-moving vehicles: A vehicle can traverse 60 metersunder two seconds while moving at 70 mph. For safe and robust drivingautomation, acute 3D object detection at these ranges is indispensable. Againstthis backdrop, we introduce faraway-frustum: a novel fusion strategy fordetecting faraway objects. The main strategy is to depend solely on the 2Dvision for recognizing object class, as object shape does not changedrastically with an increase in depth, and use pointcloud data for objectlocalization in the 3D space for faraway objects. For closer objects, we uselearned pointcloud representations instead, following state-of-the-art. Thisstrategy alleviates the main shortcoming of object detection with learnedpointcloud representations. Experiments on the KITTI dataset demonstrate thatour method outperforms state-of-the-art by a considerable margin for farawayobject detection in bird's-eye-view and 3D. Our code is open-source andpublicly available: https://github.com/dongfang-steven-yang/faraway-frustum.

Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion