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

2026-08-24

AI Summary

本文提出SiZeUp方法，通过深度顺序损失从倾斜航拍图像快速构建大规模3D城市代理模型，解决了传统方法速度慢和依赖不可靠的度量深度问题。

Abstract

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.

Problem

• 3D urban proxy
• aerial imagery
• height estimation
• depth consistency
• large-scale modeling

Innovation

• ordinal depth consistency loss
• height-from-footprint representation
• differentiable renderer
• dynamic view selection
• large-scale 3D urban proxy models