[MICCAI'2024] EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic Camera
- 2024-05-14 Our paper has been early accepted (top 11%) by MICCAI 2024!
- 2024-05-15 arxiv version is online.
Depth estimation plays a crucial role in various tasks within endoscopic surgery, including navigation, surface reconstruction, and augmented reality visualization. Despite the significant achievements of foundation models in vision tasks, including depth estimation, their direct application to the medical domain often results in suboptimal performance. This highlights the need for efficient adaptation methods to adapt these models to endoscopic depth estimation. We propose Endoscopic Depth Any Camera (EndoDAC) which is an efficient self-supervised depth estimation framework that adapts foundation models to endoscopic scenes. Specifically, we develop the Dynamic Vector-Based Low-Rank Adaptation (DV-LoRA) and employ Convolutional Neck blocks to tailor the foundational model to the surgical domain, utilizing remarkably few trainable parameters. Given that camera information is not always accessible, we also introduce a self-supervised adaptation strategy that estimates camera intrinsics using the pose encoder. Our framework is capable of being trained solely on monocular surgical videos from any camera, ensuring minimal training costs. Experiments demonstrate that our approach obtains superior performance even with fewer training epochs and unaware of the ground truth camera intrinsic.
| Method | Year | Abs Rel | Sq Rel | RMSE | RMSE log | δ | Checkpoint |
|---|---|---|---|---|---|---|---|
| Fang et al. | 2020 | 0.078 | 0.794 | 6.794 | 0.109 | 0.946 | - |
| Endo-SfM | 2021 | 0.062 | 0.606 | 5.726 | 0.093 | 0.957 | - |
| AF-SfMLeaner | 2022 | 0.059 | 0.435 | 4.925 | 0.082 | 0.974 | - |
| Yang et al. | 2024 | 0.062 | 0.558 | 5.585 | 0.090 | 0.962 | - |
| EndoDAC (Ours) | 0.052 | 0.362 | 4.464 | 0.073 | 0.979 | google_drive |
Create an environment with conda:
conda env create -f conda.yaml
conda activate endodac
Install required dependencies with pip:
pip install -r requirements.txt
Download pretrained model from: depth_anything_vitb14. Create a folder named pretrained_model in this repo and place the downloaded model in it.
Please follow AF-SfMLearner to prepare the SCARED dataset.
CUDA_VISIBLE_DEVICES=0 python train_end_to_end.py --data_path <your_data_path> --log_dir './logs'
Export ground truth depth and pose before evaluation:
CUDA_VISIBLE_DEVICES=0 python export_gt_depth.py --data_path <your_data_path> --split endovis
python export_gt_pose.py --data_path <your_data_path> --split endovis --sequence sequence2
python export_gt_pose.py --data_path <your_data_path> --split endovis --sequence sequence1
Assume to evaluate the epoch 19 weights of a depth estimation model named endodac:
CUDA_VISIBLE_DEVICES=0 python evaluate_depth.py --data_path <your_data_path> \
--load_weights_folder './logs/endodac/models/weights_19' --eval_mono
Assume to evaluate the epoch 19 weights of a pose and intrinsic estimation model named endodac:
CUDA_VISIBLE_DEVICES=0 python evaluate_pose.py --data_path <your_data_path> \
--load_weights_folder './logs/endodac/models/weights_19' --eval_mono
Our code is based on the implementation of AF-SfMLearner, Depth-Anything. We thank their excellent works.