Fully Sparse Long Range 3D Object Detection Using Range Experts and
  Multimodal Virtual Points
Ajinkya Khoche Laura Pereira Sánchez Nazre Batool Sina Sharif Mansouri Patric Jensfelt
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.