  We propose a method that combines sparse depth (LiDAR) measurements with an
intensity image and to produce a dense high-resolution depth image. As there
are few, but accurate, depth measurements from the scene, our method infers the
remaining depth values by incorporating information from the intensity image,
namely the magnitudes and directions of the identified edges, and by assuming
that the scene is composed mostly of flat surfaces. Such inference is achieved
by solving a convex optimisation problem with properly weighted regularisers
that are based on the `1-norm (specifically, on total variation). We solve the
resulting problem with a computationally efficient ADMM-based algorithm. Using
the SYNTHIA and KITTI datasets, our experiments show that the proposed method
achieves a depth reconstruction performance comparable to or better than other
model-based methods.
