arXiv:2608.22821cs.CV2026-08中稿 · SIGGRAPH

用航空影像快速构建城市3D模型,速度提升50倍以上

SiZeUp: Fast 3D Proxy from Aerial Images via Depth Ordinal Loss

论文配图:SiZeUp: Fast 3D Proxy from Aerial Images via Depth Ordinal Loss
图 1 · 摘自论文原文
  • 通过单个高度参数拉伸建筑轮廓,简化3D建模为低维优化问题
  • 采用相对深度一致性损失,实现跨视角稳定高度估计
  • 无需点云重建,适合大规模城市三维建模应用

我们提出SiZeUp,一种从校准的倾斜航空影像中直接构建大规模城市3D代理模型的快速可扩展方法。该方法采用基于底面的高程表示,将3D建筑抽象简化为仅含单一高度参数的低维优化问题。为实现高效可靠的高程估计,引入了序数深度一致性损失,强制渲染的代理在相对深度排序上与单目深度模型预测的先验一致。这一过程通过可微分渲染器实现,将参数化建筑代理映射到多视角深度图,使梯度能从深度监督传递至建筑高度。该序数公式在实践中表现稳定,避免了显式特征匹配或密集点云重建。相比依赖有误判风险的度量深度,我们的序数深度一致性损失基于相对深度,提供更可靠的跨视图信号。结合高效的动态视图选择,本方法相比当前最优代理重建流程提速23-52倍,同时保持相当的代理覆盖率和体积一致性,适用于大规模城市建模任务。

原文摘要 · Abstract (English)

We present SiZeUp, a fast and scalable approach for constructing large-scale 3D urban proxy models directly from calibrated oblique aerial imagery. Our method adopts a height-from-footprint representation, reducing 3D building abstraction to a low-dimensional optimization problem in which building footprints are extruded by a single height parameter. To enable efficient and robust height estimation, we introduce an ordinal depth consistency loss that enforces agreement between the relative depth ordering of rendered proxies and depth priors predicted by a monocular depth model. This is realized through a differentiable renderer that maps parametric building proxies into multi-view depth images, allowing gradients to be propagated from depth supervision to building heights. Our ordinal formulation produces stable optimization in practice and avoids explicit feature matching or dense point cloud reconstruction. Rather than relying on metric depth, which can be unreliable under monocular scale ambiguity, our ordinal depth consistency loss operates on relative depths, providing a more reliable signal across views. Combined with an efficient dynamic view selection, our approach achieves a 23-52$\times$ speedup over state-of-the-art proxy reconstruction pipelines while maintaining comparable proxy-level coverage and volume consistency, making it well suited for large-scale urban modeling tasks.

3D建模图像重建深度学习城市建模

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