PU-Ray: Point Cloud Upsampling via Ray Marching on Implicit Surface

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.