PPS-Ctrl: Controllable Sim-to-Real Translation for Colonoscopy Depth Estimation

• URL: http://arxiv.org/abs/2504.17067v1
• Date: Wed, 23 Apr 2025 19:28:58 GMT
• Title: PPS-Ctrl: Controllable Sim-to-Real Translation for Colonoscopy Depth Estimation
• Authors: Xinqi Xiong, Andrea Dunn Beltran, Jun Myeong Choi, Marc Niethammer, Roni Sengupta,
• Abstract summary: We propose a novel image-to-image translation framework that preserves structure while generating realistic textures from clinical data.<n>Our key innovation integrates Stable Diffusion with ControlNet, conditioned on a latent representation extracted from a Per-Pixel Shading (PPS) map.<n> Experiments show our approach produces more realistic translations and improves depth estimation over GAN-based MI-CycleGAN.
• Score: 12.549599571284304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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