While the recent advancements in deep-learning-based point cloud upsampling methods improve the input to autonomous driving systems, they still suffer from the uncertainty of denser point generation resulting from end-to-end learning. For example, due to the vague training objectives of the models, their performance depends on the point distributions of the input and the ground truth. This causes problems of domain dependency between synthetic and real-scanned point clouds and issues with substantial model sizes and dataset requirements. Additionally, many existing methods upsample point clouds with a fixed scaling rate, making them inflexible and computationally redundant. This paper addresses the above problems by proposing a ray-based upsampling approach with an arbitrary rate, where a depth prediction is made for each query ray. The method simulates the ray marching algorithm to achieve more precise and stable ray-depth predictions through implicit surface learning. The rule-based mid-point query sampling method enables a uniform output point distribution without requiring model training using the Chamfer distance loss function, which can exhibit bias towards the training dataset. Self-supervised learning becomes possible with accurate ground truths within the input point cloud. The results demonstrate the method's versatility across different domains and training scenarios with limited computational resources and training data. This allows the upsampling task to transition from academic research to real-world applications.

翻译：摘要：尽管近年来基于深度学习的点云上采样方法在提升自动驾驶系统输入质量方面取得了进展，但由于端到端学习导致的密集点生成不确定性，这些方法仍存在局限。例如，由于模型训练目标模糊，其性能依赖于输入点云与真值数据的分布特性，从而引发合成点云与真实扫描点云之间的域依赖问题，以及模型规模庞大、数据集需求苛刻等挑战。此外，现有方法多采用固定缩放率进行上采样，缺乏灵活性且存在计算冗余。本文提出一种基于光线的任意缩放率上采样方法，通过对每条查询光线进行深度预测来解决上述问题。该方法模拟光线步进算法，通过隐式表面学习实现更精确稳定的光线深度预测。基于规则的中点查询采样策略可在无需使用可能对训练数据集产生偏差的Chamfer距离损失函数的情况下，实现均匀的输出点分布。凭借输入点云中精确真值数据的支撑，自监督学习成为可能。实验结果表明，该方法在有限计算资源与训练数据条件下，仍能展现跨领域与不同训练场景的通用性，推动上采样任务从学术研究向实际应用转化。