3D object detection at long range is crucial for ensuring the safety and efficiency of self driving vehicles, 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 range limited due to sparsity at long range, 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. To address the above limitations, we investigate two ways to improve long range performance of current LiDAR based 3D detectors. First, we combine two 3D detection networks, referred to as range experts, 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 weigh the loss according to the labelled point's distance from ego vehicle. Second, we augment LiDAR scans with virtual points generated using Multimodal Virtual Points (MVP), a readily available image-based depth completion algorithm. Our experiments on the long range Argoverse2 (AV2) dataset indicate that MVP is more effective in improving long range performance, while maintaining a straightforward implementation. On the other hand, the range experts offer a computationally efficient and simpler alternative, avoiding dependency on image-based segmentation networks and perfect camera-LiDAR calibration.

翻译：长距离3D物体检测对于确保自动驾驶汽车的安全性和效率至关重要，使其能够准确感知并响应远处的物体、障碍物及潜在危险。然而，当前大多数基于激光雷达的先进方法因远距离点云稀疏而存在检测范围限制，导致自车附近与远处点之间形成领域差距。另一个相关问题是对远距离物体的标签不平衡，这抑制了深度神经网络在长距离场景下的性能。为解决上述限制，我们研究了两种提升当前基于激光雷达的3D检测器长距离性能的方法。首先，我们结合两个3D检测网络（称为距离专家），一个专攻近中距离物体，另一个专攻长距离3D检测。为在标签稀缺条件下训练长距离检测器，我们根据标签点与自车距离对损失进行加权。其次，我们利用多模态虚拟点（MVP）——一种现成的基于图像的深度补全算法——生成的虚拟点来增强激光雷达扫描数据。在长距离Argoverse2（AV2）数据集上的实验表明，MVP在提升长距离性能方面更为有效，且实现简单直接。相比之下，距离专家提供了一种计算高效且更简单的替代方案，避免了依赖基于图像的分割网络和完美的相机-激光雷达标定。