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Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency (MICCAI 2024)

Example monocular depth inference

This is the official repository for our state-of-the-art approach to monocular depth in surgical vision as presented in our paper...

    Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency
    Charlie Budd, Tom Vercauteren.
    [ arXiv ]

Using Our Models

First, install our package...

pip install git+https://github.com/charliebudd/transferring-relative-monocular-depth-to-surgical-vision

Then download one of our models weights from the release tab in this repo. We would recommend our best performer, depthanything-sup-temp.pt. The model may then be used as follows...

import torch
from torchvision.io import read_image
from torchvision.transforms.functional import resize
import matplotlib.pyplot as plt

from trmdsv import load_model

model, resize_for_model, normalise_for_model = load_model("depthanything", "weights/path.pt", "cuda")
model.eval()

image = read_image("surgical_image.png").cuda() / 255.0
original_size = image.shape[-2:]
image_for_model = normalise_for_model(resize_for_model(image.unsqueeze(0)))

with torch.no_grad():
    depth = model(image_for_model)

depth = resize(depth, original_size)

plt.subplot(121).axis("off")
plt.imshow(image.cpu().permute(1, 2, 0))
plt.subplot(122).axis("off")
plt.imshow(depth.cpu().permute(1, 2, 0))
plt.show()

Recreating Our Results

### awaiting publication ###