Scale-preserving shape reconstruction from monocular endoscope image sequences by supervised depth learning

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. A method for reconstructing a scale-preserving 3D shape from monocular endoscope image sequences is proposed. First, a network for predicting an absolute depth map from an image is trained, then with the trained network, the predicted absolute depth maps are aligned and merged to the global shape, and finally the method is applied to real endoscope image sequences.image