OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations

• URL: http://arxiv.org/abs/2406.11711v1
• Date: Mon, 17 Jun 2024 16:30:29 GMT
• Title: OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations
• Authors: Yiming Zuo, Jia Deng,
• Abstract summary: "Optimization-Guided Neural Iterations" (OGNI) is a novel framework for depth completion. OGNI-DC exhibits strong generalization, outperforming baselines on unseen datasets and across various sparsity levels. It has high accuracy, achieving state-of-the-art performance on the NYUv2 and the KITTI benchmarks.
• Score: 23.0962036039182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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