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.

翻译：深度补全任务旨在给定一幅图像和一幅稀疏深度图作为输入，生成稠密的深度图。该任务在各种下游应用中具有重要意义。本文提出了一种用于深度补全的新框架OGNI-DC。我们方法的核心是"优化引导神经迭代"（OGNI）。它由一个用于优化深度梯度场的循环单元和一个将深度梯度积分成深度图的可微深度积分器组成。OGNI-DC展现出强大的泛化能力，在未见过的数据集上以及在不同稀疏度水平下，其性能均大幅超越基线方法。此外，OGNI-DC具有很高的精度，在NYUv2和KITTI基准测试中均达到了最先进的性能。代码可在 https://github.com/princeton-vl/OGNI-DC 获取。