Scale‐preserving shape reconstruction from monocular endoscope image sequences by supervised depth learning
Takeshi Masuda, Ryusuke Sagawa, Ryo Furukawa, Hiroshi Kawasaki · Healthcare Technology Letters · 2023
Reconstructing 3D shapes from images are becoming popular, but such methods usually estimate relative depth maps with ambiguous scales. A method for reconstructing a scale-preserving 3D shape from monocular endoscope image sequences through training an absolute depth prediction network is proposed. First, a dataset of synchronized sequences of RGB images and depth maps is created using an endoscope simulator. Then, a supervised depth prediction network is trained that estimates a depth map from a RGB image minimizing the loss compared to the ground-truth depth map. The predicted depth map sequence is aligned to reconstruct a 3D shape. Finally, the proposed method is applied to a real endoscope image sequence.