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We propose a method that combines sparse depth (LiDAR) measurements with anintensity image and to produce a dense high-resolution depth image. As thereare few, but accurate, depth measurements from the scene, our method infers theremaining depth values by incorporating information from the intensity image,namely the magnitudes and directions of the identified edges, and by assumingthat the scene is composed mostly of flat surfaces. Such inference is achievedby solving a convex optimisation problem with properly weighted regularisersthat are based on the `1-norm (specifically, on total variation). We solve theresulting problem with a computationally efficient ADMM-based algorithm. Usingthe SYNTHIA and KITTI datasets, our experiments show that the proposed methodachieves a depth reconstruction performance comparable to or better than othermodel-based methods.

Image-Guided Depth Upsampling via Hessian and TV Priors