  While recent advancements in deep-learning point cloud upsampling methods
have improved the input to intelligent transportation systems, they still
suffer from issues of domain dependency between synthetic and real-scanned
point clouds. This paper addresses the above issues by proposing a new
ray-based upsampling approach with an arbitrary rate, where a depth prediction
is made for each query ray and its corresponding patch. Our novel method
simulates the sphere-tracing ray marching algorithm on the neural implicit
surface defined with an unsigned distance function (UDF) to achieve more
precise and stable ray-depth predictions by training a point-transformer-based
network. The rule-based mid-point query sampling method generates more evenly
distributed points without requiring an end-to-end model trained using a
nearest-neighbor-based reconstruction loss function, which may be biased
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 domains and training scenarios with limited
computational resources and training data. Comprehensive analyses of synthetic
and real-scanned applications provide empirical evidence for the significance
of the upsampling task across the computer vision and graphics domains to
real-world applications of ITS.
