标题： 空间提升用于密集预测 显示英文标题

标题： Spatial Lifting for Dense Prediction

摘要： 我们提出空间提升（Spatial Lifting，SL），一种用于密集预测任务的新方法。 SL 通过将标准输入（如 2D 图像）提升到更高维空间，然后使用为该高维设计的网络进行处理，例如 3D U-Net。 出人意料的是，这种维度提升使我们在与传统方法相比的基准任务上实现了良好的性能，同时降低了推理成本并显著减少了模型参数数量。 SL 框架在提升的维度上产生内在结构化的输出。 这种涌现的结构在训练期间促进了密集监督，并在测试时实现了鲁棒的、几乎无额外成本的预测质量评估。 我们在 19 个基准数据集（13 个用于语义分割，6 个用于深度估计）上验证了我们的方法，展示了具有竞争力的密集预测性能，同时将模型参数数量减少了超过 98%（在 U-Net 的情况下），并降低了推理成本。 空间提升引入了一种新的视觉建模范式，为视觉中的密集预测任务提供了更高效、准确和可靠的深度网络的有希望的路径。 显示英文摘要

摘要： We present Spatial Lifting (SL), a novel methodology for dense prediction tasks. SL operates by lifting standard inputs, such as 2D images, into a higher-dimensional space and subsequently processing them using networks designed for that higher dimension, such as a 3D U-Net. Counterintuitively, this dimensionality lifting allows us to achieve good performance on benchmark tasks compared to conventional approaches, while reducing inference costs and significantly lowering the number of model parameters. The SL framework produces intrinsically structured outputs along the lifted dimension. This emergent structure facilitates dense supervision during training and enables robust, near-zero-additional-cost prediction quality assessment at test time. We validate our approach across 19 benchmark datasets (13 for semantic segmentation and 6 for depth estimation), demonstrating competitive dense prediction performance while reducing the model parameter count by over 98% (in the U-Net case) and lowering inference costs. Spatial Lifting introduces a new vision modeling paradigm that offers a promising path toward more efficient, accurate, and reliable deep networks for dense prediction tasks in vision.