LiDAR point clouds often contain motion-induced distortions, degrading the accuracy of object appearances in the captured data. In this paper, we first characterize the underlying reasons for the point cloud distortion and show that this is present in public datasets. We find that this distortion is more pronounced in high-speed environments such as highways, as well as in multi-LiDAR configurations, a common setup for heavy vehicles. Previous work has dealt with point cloud distortion from the ego-motion but fails to consider distortion from the motion of other objects. We therefore introduce a novel undistortion pipeline, HiMo, that leverages scene flow estimation for object motion compensation, correcting the depiction of dynamic objects. We further propose an extension of a state-of-the-art self-supervised scene flow method. Due to the lack of well-established motion distortion metrics in the literature, we also propose two metrics for compensation performance evaluation: compensation accuracy at a point level and shape similarity on objects. To demonstrate the efficacy of our method, we conduct extensive experiments on the Argoverse 2 dataset and a new real-world dataset. Our new dataset is collected from heavy vehicles equipped with multi-LiDARs and on highways as opposed to mostly urban settings in the existing datasets. The source code, including all methods and the evaluation data, will be provided upon publication. See https://kin-zhang.github.io/HiMo for more details.

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.