用4D雷达生成点云，换更强大的分割主干网络提升效果
The Impact of 2D Segmentation Backbones on Point Cloud Predictions Using 4D Radar
- 用高容量2D分割主干网络改进4D雷达生成点云质量
- 最佳主干网络使点云质量比当前最优提升23.7%
- 适合关注低成本自动驾驶感知的工程师
LiDAR能提供高精度的周围环境点云，显著提升道路安全与场景理解，但其高昂成本限制了在量产车辆中的广泛应用。已有研究尝试仅使用4D雷达，通过神经网络学习生成类LiDAR的3D点云，以替代真实点云作为训练目标。其中一种代表性方法是基于RaDelft数据集，采用模块化2D卷积神经网络（CNN）主干与时间一致性网络构建模型。本文研究更高容量的分割主干网络对生成点云质量的影响。实验表明，过高的模型容量反而会降低性能，而选择最优的主干网络可使生成点云质量较当前最优水平提升23.7%。
原文摘要 · Abstract (English)
LiDAR's dense, sharp point cloud (PC) representations of the surrounding environment enable accurate perception and significantly improve road safety by offering greater scene awareness and understanding. However, LiDAR's high cost continues to restrict the broad adoption of high-level Autonomous Driving (AD) systems in commercially available vehicles. Prior research has shown progress towards circumventing the need for LiDAR by training a neural network, using LiDAR point clouds as ground truth (GT), to produce LiDAR-like 3D point clouds using only 4D Radars. One of the best examples is a neural network created to train a more efficient radar target detector with a modular 2D convolutional neural network (CNN) backbone and a temporal coherence network at its core that uses the RaDelft dataset for training (see arXiv:2406.04723). In this work, we investigate the impact of higher-capacity segmentation backbones on the quality of the produced point clouds. Our results show that while very high-capacity models may actually hurt performance, an optimal segmentation backbone can provide a 23.7% improvement over the state-of-the-art (SOTA).
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