Official implementation of the CVPR 2025 paper "Distilling Monocular Foundation Models for Fine-grained Depth Completion"

Depth completion methods often suffer in regions with sparse or missing supervision, leading to inaccurate fine-grained structures and degraded depth quality.

DMD3C introduces a novel framework that distills rich geometric priors from monocular foundation models into the depth completion pipeline. By leveraging dense knowledge from foundation models, DMD3C significantly improves depth estimation quality, particularly in regions lacking ground-truth supervision.

• Distills geometric knowledge from monocular foundation models
• Enhances fine-grained structure recovery
• Improves depth estimation in sparse and unsupervised regions
• Achieves strong performance on benchmark datasets

You can directly build the environment by running the following command if you use conda as the environment management tool.

conda env create -f environment.yml

Compile the C++ and CUDA code:

cd exts
python setup.py install

Please follow the dataset preparation instructions from:

👉 BP-Net

The structure of data directory should be:

└── datas
 └── kitti
 ├── data_depth_annotated
 │ ├── train
 │ └── val
 ├── data_depth_velodyne
 │ ├── train
 │ └── val
 ├── raw
 │ ├── 2011_09_26
 │ ├── 2011_09_28
 │ ├── 2011_09_29
 │ ├── 2011_09_30
 │ └── 2011_10_03
 ├── test_depth_completion_anonymous
 │ ├── image
 │ ├── intrinsics
 │ └── velodyne_raw
 └── val_selection_cropped
 ├── groundtruth_depth
 ├── image
 ├── intrinsics
 └── velodyne_raw

Run the training script:

bash train.sh

Our models are trained on 8 GPU workstation with Nvidia GTX 4090 (48G).

Download pretrained checkpoints from:

👉 Hugging Face

Place the .pth file into "./checkpoints/PMP_Residual_Norm_ssil_KITTI/"

Generate predictions and submit results to the KITTI online benchmark:

bash submission.sh

The results will be save into "./results" folder.

To facilitate fair and reproducible evaluation of depth completion methods, we build DCVerse, a unified depth completion benchmark that standardizes the experimental settings across different methods and datasets.

DCVerse addresses inconsistencies commonly found in previous evaluations, including:

• Unified input image resolution
• Consistent sparse point sampling density
• Standardized sparse sampling strategies
• Unified evaluation metrics
• Cross-dataset evaluation protocol

The benchmark enables more reliable and direct comparisons between different depth completion methods.

The benchmark and processed data can be found at:

👉 DCVerse on Hugging Face

#!/bin/bash
datasets=(
 ETH3D_SfM_Indoor_test
 ETH3D_SfM_Outdoor_test
 KITTIDC_test_LiDAR_64
 KITTIDC_test_LiDAR_32
 KITTIDC_test_LiDAR_16
 KITTIDC_test_LiDAR_8
 VOID_sample1500
 VOID_sample500
 VOID_sample150
 NYU_test_500
 NYU_test_200
 NYU_test_100
 NYU_test_50
 DDAD_val
)
mkdir -p results
for dataset in "${datasets[@]}"
do
 echo "======================================"
 echo "Running dataset: ${dataset}"
 echo "======================================"
 python test.py \
 gpus=[0] \
 name=PMP_Residual_Norm_ssil_KITTI_${dataset} \
 ++chpt=PMP_Residual_Norm_ssil_KITTI \
 net=PMP_Residual_Norm_fast \
 num_workers=4 \
 data=UNI \
 data.testset.mode=test \
 data.path=/PATH-TO-DATA/${dataset} \
 test_batch_size=1 \
 metric=MetricALL \
 ++save=true \
 2>&1 | tee "results/${dataset}.log"
done
echo "All tests finished."

The adapted implementations are available in the benchmarks/ directory.

The current benchmark includes implementations of the following representative depth completion methods:

We continuously maintain and extend the benchmark to include newly proposed methods and stronger baselines, providing a unified platform for fair and reproducible depth completion evaluation.

If you find our work useful for your research, please consider citing:

@inproceedings{liang2025distilling,
 title={Distilling Monocular Foundation Models for Fine-grained Depth Completion},
 author={Liang, Yingping and Hu, Yutao and Shao, Wenqi and Fu, Ying},
 booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
 pages={22254--22265},
 year={2025}
}

This project is built upon and inspired by:

• BP-Net

• OMNI-DC

We sincerely thank the authors for making their code publicly available.