  Depth completion is the task of generating a dense depth map given an image
and a sparse depth map as inputs. It has important applications in various
downstream tasks. In this paper, we present OGNI-DC, a novel framework for
depth completion. The key to our method is "Optimization-Guided Neural
Iterations" (OGNI). It consists of a recurrent unit that refines a depth
gradient field and a differentiable depth integrator that integrates the depth
gradients into a depth map. OGNI-DC exhibits strong generalization,
outperforming baselines by a large margin on unseen datasets and across various
sparsity levels. Moreover, OGNI-DC has high accuracy, achieving
state-of-the-art performance on the NYUv2 and the KITTI benchmarks. Code is
available at https://github.com/princeton-vl/OGNI-DC.
