OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations
<|reference_start|>OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations: 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.<|reference_end|>
@article{zuo2024ogni-dc:,
    title={OGNI-DC: Robust Depth Completion with Optimization-Guided Neural
  Iterations},
    author={Yiming Zuo, Jia Deng},
    journal={arXiv preprint arXiv:2406.11711},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11711},
    primaryClass={cs.CV}
}