  Super-resolution of LiDAR range images is crucial to improving many
downstream tasks such as object detection, recognition, and tracking. While
deep learning has made a remarkable advances in super-resolution techniques,
typical convolutional architectures limit upscaling factors to specific output
resolutions in training. Recent work has shown that a continuous representation
of an image and learning its implicit function enable almost limitless
upscaling. However, the detailed approach, predicting values (depths) for
neighbor pixels in the input and then linearly interpolating them, does not
best fit the LiDAR range images since it does not fill the unmeasured details
but creates a new image with regression in a high-dimensional space. In
addition, the linear interpolation blurs sharp edges providing important
boundary information of objects in 3-D points. To handle these problems, we
propose a novel network, Implicit LiDAR Network (ILN), which learns not the
values per pixels but weights in the interpolation so that the superresolution
can be done by blending the input pixel depths but with non-linear weights.
Also, the weights can be considered as attentions from the query to the
neighbor pixels, and thus an attention module in the recent Transformer
architecture can be leveraged. Our experiments with a novel large-scale
synthetic dataset demonstrate that the proposed network reconstructs more
accurately than the state-of-the-art methods, achieving much faster convergence
in training.
