3D object detection at long-range is crucial for ensuring the safety and efficiency of self-driving cars, allowing them to accurately perceive and react to objects, obstacles, and potential hazards from a distance. But most current state-of-the-art LiDAR based methods are limited by the sparsity of range sensors, which generates a form of domain gap between points closer to and farther away from the ego vehicle. Another related problem is the label imbalance for faraway objects, which inhibits the performance of Deep Neural Networks at long-range. Although image features could be beneficial for long-range detections, and some recently proposed multimodal methods incorporate image features, they do not scale well computationally at long ranges or are limited by depth estimation accuracy. To address the above limitations, we propose to combine two LiDAR based 3D detection networks, one specializing at near to mid-range objects, and one at long-range 3D detection. To train a detector at long range under a scarce label regime, we further propose to weigh the loss according to the labelled objects' distance from ego vehicle. To mitigate the LiDAR sparsity issue, we leverage Multimodal Virtual Points (MVP), an image based depth completion algorithm, to enrich our data with virtual points. Our method, combining two range experts trained with MVP, which we refer to as RangeFSD, achieves state-of-the-art performance on the Argoverse2 (AV2) dataset, with improvements at long range. The code will be released soon.

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