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arXiv · 2603.10584

Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion

Jakub Gregorek·Paraskevas Pegios·Nando Metzger·Konrad Schindler·Theodora Kontogianni·Lazaros Nalpantidis
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Abstract

We introduce Marigold-SSD, a single-step, late-fusion depth completion framework that leverages strong diffusion priors while eliminating the costly test-time optimization typically associated with diffusion-based methods. By shifting computational burden from inference to finetuning, our approach enables efficient and robust 3D perception under real-world latency constraints. Marigold-SSD achieves significantly faster inference with a training cost of only 4.5 GPU days. We evaluate our method across four indoor and two outdoor benchmarks, demonstrating strong cross-domain generalization and zero-shot performance compared to existing depth completion approaches. Our approach significantly narrows the efficiency gap between diffusion-based and discriminative models. Finally, we challenge common evaluation protocols by analyzing performance under varying input sparsity levels. Page: https://dtu-pas.github.io/marigold-ssd/

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Jakub Gregorek, Paraskevas Pegios, Nando Metzger, Konrad Schindler, Theodora Kontogianni, Lazaros Nalpantidis. 2026-03-11. Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion. https://arxiv.org/abs/2603.10584

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