单步扩散模型实现零样本深度补全，推理速度提升显著
Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion
- 采用单步延迟融合框架，将计算负担从推理转移到微调阶段
- 仅需4.5天GPU训练，推理速度远超传统扩散方法
- 在6个场景中展现强跨域泛化能力，适合实时3D感知应用
我们提出Marigold-SSD，一种单步、延迟融合的深度补全框架，利用强大的扩散先验，同时消除扩散方法通常依赖的昂贵测试时优化。通过将计算负担从推理阶段转移至微调阶段，该方法在真实延迟约束下实现了高效且稳健的3D感知。Marigold-SSD在仅4.5 GPU天的训练成本下实现了显著更快的推理速度。我们在四个室内和两个室外基准上评估了该方法，相比现有深度补全技术展现出优异的跨域泛化能力和零样本性能。该方法显著缩小了基于扩散与判别式模型之间的效率差距。最后，我们通过分析不同输入稀疏度下的表现，挑战了常见的评估协议。
原文摘要 · Abstract (English)
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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