@inproceedings{df6fa819bf154a5eb518872981f93c1c,
title = "OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations",
abstract = " Depth completion is the task of generating a dense depth map given an image and a sparse depth map as inputs. 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.",
keywords = "Depth Completion, Optimization-inspired Design",
author = "Yiming Zuo and Jia Deng",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 18th European Conference on Computer Vision, ECCV 2024 ; Conference date: 29-09-2024 Through 04-10-2024",
year = "2025",
doi = "10.1007/978-3-031-72646-0_5",
language = "English (US)",
isbn = "9783031726453",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "78--95",
editor = "Ale{\v s} Leonardis and Elisa Ricci and Stefan Roth and Olga Russakovsky and Torsten Sattler and G{\"u}l Varol",
booktitle = "Computer Vision – ECCV 2024 - 18th European Conference, Proceedings",
address = "Germany",
}