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.

最近深度学习点云上采样方法在改善自动驾驶系统的输入方面取得了进展，但仍然存在由于端到端学习导致的更密集点生成的不确定性问题。本文提出了一种基于射线的任意倍率上采样方法，通过隐式表面学习来模拟射线行进算法，实现更精确和稳定的射线深度预测。中点查询采样方法基于规则，能够在不需要使用 Chamfer 距离损失函数进行模型训练的情况下实现均匀的输出点分布。准确的输入点云内的真实地面信息使得自我监督学习成为可能。通过有限的计算资源和训练数据，结果证明了该方法在不同领域和训练场景中的多功能性。这使得上采样任务从学术研究过渡到实际应用。