PPS-Ctrl：用于结肠镜深度估计的可控模拟到真实转换
PPS-Ctrl: Controllable Sim-to-Real Translation for Colonoscopy Depth Estimation
AI总结：
提出PPS-Ctrl框架，结合Stable Diffusion与ControlNet，利用逐像素着色图作为结构约束，实现结肠镜图像从模拟到真实的转换，提升深度估计性能。
摘要：
准确的深度估计可增强内窥镜导航和诊断能力，但在临床环境中获取真实深度具有挑战性。合成数据集常用于训练，但域差距限制了其对真实数据的泛化能力。我们提出了一种新颖的图像到图像转换框架，该框架在从临床数据生成真实纹理的同时保留结构。我们的关键创新是将Stable Diffusion与ControlNet集成，并以从逐像素着色（PPS）图提取的潜在表示作为条件。PPS捕获表面光照效果，提供比深度图更强的结构约束。实验表明，我们的方法生成的转换更真实，且深度估计优于基于GAN的MI-CycleGAN。我们的代码公开于https://github.com/anaxqx/PPS-Ctrl。
英文摘要：
Accurate depth estimation enhances endoscopy navigation and diagnostics, but obtaining ground-truth depth in clinical settings is challenging. Synthetic datasets are often used for training, yet the domain gap limits generalization to real data. We propose a novel image-to-image translation framework that preserves structure while generating realistic textures from clinical data. Our key innovation integrates Stable Diffusion with ControlNet, conditioned on a latent representation extracted from a Per-Pixel Shading (PPS) map. PPS captures surface lighting effects, providing a stronger structural constraint than depth maps. Experiments show our approach produces more realistic translations and improves depth estimation over GAN-based MI-CycleGAN. Our code is publicly accessible at https://github.com/anaxqx/PPS-Ctrl.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
