Image-Guided Depth Upsampling via Hessian and TV Priors
This work addresses depth upsampling for applications like autonomous driving or robotics, but it is incremental as it builds on existing model-based methods with specific regularizers.
The paper tackles the problem of generating dense high-resolution depth images from sparse LiDAR measurements and intensity images by solving a convex optimization problem with total variation regularizers, achieving depth reconstruction performance comparable to or better than other model-based methods on SYNTHIA and KITTI datasets.
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.