用局部无序图像提升渲染优化中的梯度稀疏问题
Locally Orderless Images for Optimization in Differentiable Rendering
- 将像素转为局部强度直方图，保留外观变化细节
- 通过最小化直方图距离实现逆渲染，提升参数优化收敛性
- 适用于真实与合成数据的多种反向渲染任务
不同微分渲染中的优化常涉及导致图像空间运动的场景参数。这些参数的梯度往往稀疏，导致收敛困难。现有方法通过拓扑导数或拉格朗日导数等代理梯度缓解此问题，但依赖渲染简化假设。多分辨率图像金字塔是另一方案，但实际应用中不可靠。本文提出使用局部无序图像——每个像素映射为保留局部外观变化的强度直方图。基于最小化直方图距离的逆渲染目标，该方法扩展了对稀疏梯度的支持，有效恢复最优参数。在合成与真实数据上验证了其在多种逆问题中的有效性。
原文摘要 · Abstract (English)
Problems in differentiable rendering often involve optimizing scene parameters that cause motion in image space. The gradients for such parameters tend to be sparse, leading to poor convergence. While existing methods address this sparsity through proxy gradients such as topological derivatives or lagrangian derivatives, they make simplifying assumptions about rendering. Multi-resolution image pyramids offer an alternative approach but prove unreliable in practice. We introduce a method that uses locally orderless images, where each pixel maps to a histogram of intensities that preserves local variations in appearance. Using an inverse rendering objective that minimizes histogram distance, our method extends support for sparsely defined image gradients and recovers optimal parameters. We validate our method on various inverse problems using both synthetic and real data.
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