DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Official Code for the CVPR 2025 Paper
"[CVPR 2025] Distilling Monocular Foundation Model for Fine-grained Depth Completion"

📄 Paper on arXiv

Important Update!

Please refer to our new repo: https://github.com/Sharpiless/DMD3Cpp for detailed training scripts and benchmarks!

🆕 Update Log

[2025.04.23] We have released the 2rd stage training code! 🎉
[2025.04.11] We have released the inference code! 🎉

✅ To Do

📦 Easy-to-use data generation pipeline

🔍 Overview

DMD³C introduces a novel framework for fine-grained depth completion by distilling knowledge from monocular foundation models. This approach significantly enhances depth estimation accuracy in sparse data, especially in regions without ground-truth supervision.

🚀 Getting Started

1. Clone Base Repository

git clone https://github.com/kakaxi314/BP-Net.git

2. Copy This Repo into the BP-Net Directory

cp DMD3C/* BP-Net/
cd BP-Net/DMD3C/

3. Prepare KITTI Raw Data

Download any sequence from the KITTI Raw dataset, which includes:

Camera intrinsics
Velodyne point cloud
Image sequences

Make sure the structure follows the standard KITTI format.

4. Modify the Sequence in `demo.py` for Inference

Open demo.py and go to line 338, where you can modify the input sequence path according to your downloaded KITTI data.

# demo.py (Line 338)sequence="/path/to/your/kitti/sequence"

Download pre-trained weights:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/dmd3c_distillation_depth_anything_v2.pth
mv dmd3c_distillation_depth_anything_v2.pth checkpoints

Run inference:

bash demo.sh

You will get results like this:

5. Train on KITTI

Runing monocular depth estimation for all KITTI-raw images. Data structure:

├── datas/kitti/raw/
│ ├── 2011_09_26
│ │ ├── 2011_09_26_drive_0001_sync
│ │ │ ├── image_02
│ │ │ │ ├── data/*.png
│ │ │ │ ├── disp/*.png
│ │ │ ├── image_03
│ │ ├── 2011_09_26_drive_0002_sync.......

Where disparity images are stored in gray-scale.

Download pre-trained checkpoitns:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/pretrained_mixed_singleview_256.pth
mv pretrained_mixed_singleview_256.pth checkpoints

Zero-shot preformance on KITTI valiation set:

Training Data	RMSE	MAE	iRMSE	REL
Single-view Images	1.4251	0.3722	0.0056	0.0235

Run metric-finetuning on KITTI dataset:

torchrun --nproc_per_node=4 --master_port 4321 train_distill.py \
gpus=[0,1,2,3] num_workers=4 name=DMD3D_BP_KITTI \
++chpt=checkpoints/pretrained_mixed_singleview_256.pth \
net=PMP data=KITTI \
lr=5e-4 train_batch_size=2 test_batch_size=1 \
sched/lr=NoiseOneCycleCosMo sched.lr.policy.max_momentum=0.90 \
nepoch=30 test_epoch=25 ++net.sbn=true

DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Official Code for the CVPR 2025 Paper
"[CVPR 2025] Distilling Monocular Foundation Model for Fine-grained Depth Completion"

📄 Paper on arXiv

Important Update!

Please refer to our new repo: https://github.com/Sharpiless/DMD3Cpp for detailed training scripts and benchmarks!

🆕 Update Log

[2025.04.23] We have released the 2rd stage training code! 🎉
[2025.04.11] We have released the inference code! 🎉

✅ To Do

📦 Easy-to-use data generation pipeline

🔍 Overview

DMD³C introduces a novel framework for fine-grained depth completion by distilling knowledge from monocular foundation models. This approach significantly enhances depth estimation accuracy in sparse data, especially in regions without ground-truth supervision.

🚀 Getting Started

1. Clone Base Repository

git clone https://github.com/kakaxi314/BP-Net.git

2. Copy This Repo into the BP-Net Directory

cp DMD3C/* BP-Net/
cd BP-Net/DMD3C/

3. Prepare KITTI Raw Data

Download any sequence from the KITTI Raw dataset, which includes:

Camera intrinsics
Velodyne point cloud
Image sequences

Make sure the structure follows the standard KITTI format.

4. Modify the Sequence in `demo.py` for Inference

Open demo.py and go to line 338, where you can modify the input sequence path according to your downloaded KITTI data.

# demo.py (Line 338)sequence="/path/to/your/kitti/sequence"

Download pre-trained weights:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/dmd3c_distillation_depth_anything_v2.pth
mv dmd3c_distillation_depth_anything_v2.pth checkpoints

Run inference:

bash demo.sh

You will get results like this:

5. Train on KITTI

Runing monocular depth estimation for all KITTI-raw images. Data structure:

├── datas/kitti/raw/
│ ├── 2011_09_26
│ │ ├── 2011_09_26_drive_0001_sync
│ │ │ ├── image_02
│ │ │ │ ├── data/*.png
│ │ │ │ ├── disp/*.png
│ │ │ ├── image_03
│ │ ├── 2011_09_26_drive_0002_sync.......

Where disparity images are stored in gray-scale.

Download pre-trained checkpoitns:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/pretrained_mixed_singleview_256.pth
mv pretrained_mixed_singleview_256.pth checkpoints

Zero-shot preformance on KITTI valiation set:

Training Data	RMSE	MAE	iRMSE	REL
Single-view Images	1.4251	0.3722	0.0056	0.0235

Run metric-finetuning on KITTI dataset:

torchrun --nproc_per_node=4 --master_port 4321 train_distill.py \
gpus=[0,1,2,3] num_workers=4 name=DMD3D_BP_KITTI \
++chpt=checkpoints/pretrained_mixed_singleview_256.pth \
net=PMP data=KITTI \
lr=5e-4 train_batch_size=2 test_batch_size=1 \
sched/lr=NoiseOneCycleCosMo sched.lr.policy.max_momentum=0.90 \
nepoch=30 test_epoch=25 ++net.sbn=true

DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Official Code for the CVPR 2025 Paper
"[CVPR 2025] Distilling Monocular Foundation Model for Fine-grained Depth Completion"

📄 Paper on arXiv

Important Update!

Please refer to our new repo: https://github.com/Sharpiless/DMD3Cpp for detailed training scripts and benchmarks!

🆕 Update Log

[2025.04.23] We have released the 2rd stage training code! 🎉
[2025.04.11] We have released the inference code! 🎉

✅ To Do

📦 Easy-to-use data generation pipeline

🔍 Overview

DMD³C introduces a novel framework for fine-grained depth completion by distilling knowledge from monocular foundation models. This approach significantly enhances depth estimation accuracy in sparse data, especially in regions without ground-truth supervision.

🚀 Getting Started

1. Clone Base Repository

git clone https://github.com/kakaxi314/BP-Net.git

2. Copy This Repo into the BP-Net Directory

cp DMD3C/* BP-Net/
cd BP-Net/DMD3C/

3. Prepare KITTI Raw Data

Download any sequence from the KITTI Raw dataset, which includes:

Camera intrinsics
Velodyne point cloud
Image sequences

Make sure the structure follows the standard KITTI format.

4. Modify the Sequence in `demo.py` for Inference

Open demo.py and go to line 338, where you can modify the input sequence path according to your downloaded KITTI data.

# demo.py (Line 338)sequence="/path/to/your/kitti/sequence"

Download pre-trained weights:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/dmd3c_distillation_depth_anything_v2.pth
mv dmd3c_distillation_depth_anything_v2.pth checkpoints

Run inference:

bash demo.sh

You will get results like this:

5. Train on KITTI

Runing monocular depth estimation for all KITTI-raw images. Data structure:

├── datas/kitti/raw/
│ ├── 2011_09_26
│ │ ├── 2011_09_26_drive_0001_sync
│ │ │ ├── image_02
│ │ │ │ ├── data/*.png
│ │ │ │ ├── disp/*.png
│ │ │ ├── image_03
│ │ ├── 2011_09_26_drive_0002_sync.......

Where disparity images are stored in gray-scale.

Download pre-trained checkpoitns:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/pretrained_mixed_singleview_256.pth
mv pretrained_mixed_singleview_256.pth checkpoints

Zero-shot preformance on KITTI valiation set:

Training Data	RMSE	MAE	iRMSE	REL
Single-view Images	1.4251	0.3722	0.0056	0.0235

Run metric-finetuning on KITTI dataset:

torchrun --nproc_per_node=4 --master_port 4321 train_distill.py \
gpus=[0,1,2,3] num_workers=4 name=DMD3D_BP_KITTI \
++chpt=checkpoints/pretrained_mixed_singleview_256.pth \
net=PMP data=KITTI \
lr=5e-4 train_batch_size=2 test_batch_size=1 \
sched/lr=NoiseOneCycleCosMo sched.lr.policy.max_momentum=0.90 \
nepoch=30 test_epoch=25 ++net.sbn=true

DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Official Code for the CVPR 2025 Paper
"[CVPR 2025] Distilling Monocular Foundation Model for Fine-grained Depth Completion"

📄 Paper on arXiv

Important Update!

Please refer to our new repo: https://github.com/Sharpiless/DMD3Cpp for detailed training scripts and benchmarks!

🆕 Update Log

[2025.04.23] We have released the 2rd stage training code! 🎉
[2025.04.11] We have released the inference code! 🎉

✅ To Do

📦 Easy-to-use data generation pipeline

🔍 Overview

DMD³C introduces a novel framework for fine-grained depth completion by distilling knowledge from monocular foundation models. This approach significantly enhances depth estimation accuracy in sparse data, especially in regions without ground-truth supervision.

🚀 Getting Started

1. Clone Base Repository

git clone https://github.com/kakaxi314/BP-Net.git

2. Copy This Repo into the BP-Net Directory

cp DMD3C/* BP-Net/
cd BP-Net/DMD3C/

3. Prepare KITTI Raw Data

Download any sequence from the KITTI Raw dataset, which includes:

Camera intrinsics
Velodyne point cloud
Image sequences

Make sure the structure follows the standard KITTI format.

4. Modify the Sequence in `demo.py` for Inference

Open demo.py and go to line 338, where you can modify the input sequence path according to your downloaded KITTI data.

# demo.py (Line 338)sequence="/path/to/your/kitti/sequence"

Download pre-trained weights:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/dmd3c_distillation_depth_anything_v2.pth
mv dmd3c_distillation_depth_anything_v2.pth checkpoints

Run inference:

bash demo.sh

You will get results like this:

5. Train on KITTI

Runing monocular depth estimation for all KITTI-raw images. Data structure:

├── datas/kitti/raw/
│ ├── 2011_09_26
│ │ ├── 2011_09_26_drive_0001_sync
│ │ │ ├── image_02
│ │ │ │ ├── data/*.png
│ │ │ │ ├── disp/*.png
│ │ │ ├── image_03
│ │ ├── 2011_09_26_drive_0002_sync.......

Where disparity images are stored in gray-scale.

Download pre-trained checkpoitns:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/pretrained_mixed_singleview_256.pth
mv pretrained_mixed_singleview_256.pth checkpoints

Zero-shot preformance on KITTI valiation set:

Training Data	RMSE	MAE	iRMSE	REL
Single-view Images	1.4251	0.3722	0.0056	0.0235

Run metric-finetuning on KITTI dataset:

torchrun --nproc_per_node=4 --master_port 4321 train_distill.py \
gpus=[0,1,2,3] num_workers=4 name=DMD3D_BP_KITTI \
++chpt=checkpoints/pretrained_mixed_singleview_256.pth \
net=PMP data=KITTI \
lr=5e-4 train_batch_size=2 test_batch_size=1 \
sched/lr=NoiseOneCycleCosMo sched.lr.policy.max_momentum=0.90 \
nepoch=30 test_epoch=25 ++net.sbn=true

DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Official Code for the CVPR 2025 Paper
"[CVPR 2025] Distilling Monocular Foundation Model for Fine-grained Depth Completion"

📄 Paper on arXiv

Important Update!

Please refer to our new repo: https://github.com/Sharpiless/DMD3Cpp for detailed training scripts and benchmarks!

🆕 Update Log

[2025.04.23] We have released the 2rd stage training code! 🎉
[2025.04.11] We have released the inference code! 🎉

✅ To Do

📦 Easy-to-use data generation pipeline

🔍 Overview

DMD³C introduces a novel framework for fine-grained depth completion by distilling knowledge from monocular foundation models. This approach significantly enhances depth estimation accuracy in sparse data, especially in regions without ground-truth supervision.

🚀 Getting Started

1. Clone Base Repository

git clone https://github.com/kakaxi314/BP-Net.git

2. Copy This Repo into the BP-Net Directory

cp DMD3C/* BP-Net/
cd BP-Net/DMD3C/

3. Prepare KITTI Raw Data

Download any sequence from the KITTI Raw dataset, which includes:

Camera intrinsics
Velodyne point cloud
Image sequences

Make sure the structure follows the standard KITTI format.

4. Modify the Sequence in `demo.py` for Inference

Open demo.py and go to line 338, where you can modify the input sequence path according to your downloaded KITTI data.

# demo.py (Line 338)sequence="/path/to/your/kitti/sequence"

Download pre-trained weights:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/dmd3c_distillation_depth_anything_v2.pth
mv dmd3c_distillation_depth_anything_v2.pth checkpoints

Run inference:

bash demo.sh

You will get results like this:

5. Train on KITTI

Runing monocular depth estimation for all KITTI-raw images. Data structure:

├── datas/kitti/raw/
│ ├── 2011_09_26
│ │ ├── 2011_09_26_drive_0001_sync
│ │ │ ├── image_02
│ │ │ │ ├── data/*.png
│ │ │ │ ├── disp/*.png
│ │ │ ├── image_03
│ │ ├── 2011_09_26_drive_0002_sync.......

Where disparity images are stored in gray-scale.

Download pre-trained checkpoitns:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/pretrained_mixed_singleview_256.pth
mv pretrained_mixed_singleview_256.pth checkpoints

Zero-shot preformance on KITTI valiation set:

Training Data	RMSE	MAE	iRMSE	REL
Single-view Images	1.4251	0.3722	0.0056	0.0235

Run metric-finetuning on KITTI dataset:

torchrun --nproc_per_node=4 --master_port 4321 train_distill.py \
gpus=[0,1,2,3] num_workers=4 name=DMD3D_BP_KITTI \
++chpt=checkpoints/pretrained_mixed_singleview_256.pth \
net=PMP data=KITTI \
lr=5e-4 train_batch_size=2 test_batch_size=1 \
sched/lr=NoiseOneCycleCosMo sched.lr.policy.max_momentum=0.90 \
nepoch=30 test_epoch=25 ++net.sbn=true

DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Official Code for the CVPR 2025 Paper
"[CVPR 2025] Distilling Monocular Foundation Model for Fine-grained Depth Completion"

📄 Paper on arXiv

Important Update!

Please refer to our new repo: https://github.com/Sharpiless/DMD3Cpp for detailed training scripts and benchmarks!

🆕 Update Log

[2025.04.23] We have released the 2rd stage training code! 🎉
[2025.04.11] We have released the inference code! 🎉

✅ To Do

📦 Easy-to-use data generation pipeline

🔍 Overview

DMD³C introduces a novel framework for fine-grained depth completion by distilling knowledge from monocular foundation models. This approach significantly enhances depth estimation accuracy in sparse data, especially in regions without ground-truth supervision.

🚀 Getting Started

1. Clone Base Repository

git clone https://github.com/kakaxi314/BP-Net.git

2. Copy This Repo into the BP-Net Directory

cp DMD3C/* BP-Net/
cd BP-Net/DMD3C/

3. Prepare KITTI Raw Data

Download any sequence from the KITTI Raw dataset, which includes:

Camera intrinsics
Velodyne point cloud
Image sequences

Make sure the structure follows the standard KITTI format.

4. Modify the Sequence in `demo.py` for Inference

Open demo.py and go to line 338, where you can modify the input sequence path according to your downloaded KITTI data.

# demo.py (Line 338)sequence="/path/to/your/kitti/sequence"

Download pre-trained weights:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/dmd3c_distillation_depth_anything_v2.pth
mv dmd3c_distillation_depth_anything_v2.pth checkpoints

Run inference:

bash demo.sh

You will get results like this:

5. Train on KITTI

Runing monocular depth estimation for all KITTI-raw images. Data structure:

├── datas/kitti/raw/
│ ├── 2011_09_26
│ │ ├── 2011_09_26_drive_0001_sync
│ │ │ ├── image_02
│ │ │ │ ├── data/*.png
│ │ │ │ ├── disp/*.png
│ │ │ ├── image_03
│ │ ├── 2011_09_26_drive_0002_sync.......

Where disparity images are stored in gray-scale.

Download pre-trained checkpoitns:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/pretrained_mixed_singleview_256.pth
mv pretrained_mixed_singleview_256.pth checkpoints

Zero-shot preformance on KITTI valiation set:

Training Data	RMSE	MAE	iRMSE	REL
Single-view Images	1.4251	0.3722	0.0056	0.0235

Run metric-finetuning on KITTI dataset:

torchrun --nproc_per_node=4 --master_port 4321 train_distill.py \
gpus=[0,1,2,3] num_workers=4 name=DMD3D_BP_KITTI \
++chpt=checkpoints/pretrained_mixed_singleview_256.pth \
net=PMP data=KITTI \
lr=5e-4 train_batch_size=2 test_batch_size=1 \
sched/lr=NoiseOneCycleCosMo sched.lr.policy.max_momentum=0.90 \
nepoch=30 test_epoch=25 ++net.sbn=true

DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Official Code for the CVPR 2025 Paper
"[CVPR 2025] Distilling Monocular Foundation Model for Fine-grained Depth Completion"

📄 Paper on arXiv

Important Update!

Please refer to our new repo: https://github.com/Sharpiless/DMD3Cpp for detailed training scripts and benchmarks!

🆕 Update Log

[2025.04.23] We have released the 2rd stage training code! 🎉
[2025.04.11] We have released the inference code! 🎉

✅ To Do

📦 Easy-to-use data generation pipeline

🔍 Overview

DMD³C introduces a novel framework for fine-grained depth completion by distilling knowledge from monocular foundation models. This approach significantly enhances depth estimation accuracy in sparse data, especially in regions without ground-truth supervision.

🚀 Getting Started

1. Clone Base Repository

git clone https://github.com/kakaxi314/BP-Net.git

2. Copy This Repo into the BP-Net Directory

cp DMD3C/* BP-Net/
cd BP-Net/DMD3C/

3. Prepare KITTI Raw Data

Download any sequence from the KITTI Raw dataset, which includes:

Camera intrinsics
Velodyne point cloud
Image sequences

Make sure the structure follows the standard KITTI format.

4. Modify the Sequence in `demo.py` for Inference

Open demo.py and go to line 338, where you can modify the input sequence path according to your downloaded KITTI data.

# demo.py (Line 338)sequence="/path/to/your/kitti/sequence"

Download pre-trained weights:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/dmd3c_distillation_depth_anything_v2.pth
mv dmd3c_distillation_depth_anything_v2.pth checkpoints

Run inference:

bash demo.sh

You will get results like this:

5. Train on KITTI

Runing monocular depth estimation for all KITTI-raw images. Data structure:

├── datas/kitti/raw/
│ ├── 2011_09_26
│ │ ├── 2011_09_26_drive_0001_sync
│ │ │ ├── image_02
│ │ │ │ ├── data/*.png
│ │ │ │ ├── disp/*.png
│ │ │ ├── image_03
│ │ ├── 2011_09_26_drive_0002_sync.......

Where disparity images are stored in gray-scale.

Download pre-trained checkpoitns:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/pretrained_mixed_singleview_256.pth
mv pretrained_mixed_singleview_256.pth checkpoints

Zero-shot preformance on KITTI valiation set:

Training Data	RMSE	MAE	iRMSE	REL
Single-view Images	1.4251	0.3722	0.0056	0.0235

Run metric-finetuning on KITTI dataset:

torchrun --nproc_per_node=4 --master_port 4321 train_distill.py \
gpus=[0,1,2,3] num_workers=4 name=DMD3D_BP_KITTI \
++chpt=checkpoints/pretrained_mixed_singleview_256.pth \
net=PMP data=KITTI \
lr=5e-4 train_batch_size=2 test_batch_size=1 \
sched/lr=NoiseOneCycleCosMo sched.lr.policy.max_momentum=0.90 \
nepoch=30 test_epoch=25 ++net.sbn=true

DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Official Code for the CVPR 2025 Paper
"[CVPR 2025] Distilling Monocular Foundation Model for Fine-grained Depth Completion"

📄 Paper on arXiv

Important Update!

Please refer to our new repo: https://github.com/Sharpiless/DMD3Cpp for detailed training scripts and benchmarks!

🆕 Update Log

[2025.04.23] We have released the 2rd stage training code! 🎉
[2025.04.11] We have released the inference code! 🎉

✅ To Do

📦 Easy-to-use data generation pipeline

🔍 Overview

DMD³C introduces a novel framework for fine-grained depth completion by distilling knowledge from monocular foundation models. This approach significantly enhances depth estimation accuracy in sparse data, especially in regions without ground-truth supervision.

🚀 Getting Started

1. Clone Base Repository

git clone https://github.com/kakaxi314/BP-Net.git

2. Copy This Repo into the BP-Net Directory

cp DMD3C/* BP-Net/
cd BP-Net/DMD3C/

3. Prepare KITTI Raw Data

Download any sequence from the KITTI Raw dataset, which includes:

Camera intrinsics
Velodyne point cloud
Image sequences

Make sure the structure follows the standard KITTI format.

4. Modify the Sequence in `demo.py` for Inference

Open demo.py and go to line 338, where you can modify the input sequence path according to your downloaded KITTI data.

# demo.py (Line 338)sequence="/path/to/your/kitti/sequence"

Download pre-trained weights:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/dmd3c_distillation_depth_anything_v2.pth
mv dmd3c_distillation_depth_anything_v2.pth checkpoints

Run inference:

bash demo.sh

You will get results like this:

5. Train on KITTI

Runing monocular depth estimation for all KITTI-raw images. Data structure:

├── datas/kitti/raw/
│ ├── 2011_09_26
│ │ ├── 2011_09_26_drive_0001_sync
│ │ │ ├── image_02
│ │ │ │ ├── data/*.png
│ │ │ │ ├── disp/*.png
│ │ │ ├── image_03
│ │ ├── 2011_09_26_drive_0002_sync.......

Where disparity images are stored in gray-scale.

Download pre-trained checkpoitns:

wget https://github.com/Sharpiless/DMD3C/releases/download/pretrain-checkpoints/pretrained_mixed_singleview_256.pth
mv pretrained_mixed_singleview_256.pth checkpoints

Zero-shot preformance on KITTI valiation set:

Training Data	RMSE	MAE	iRMSE	REL
Single-view Images	1.4251	0.3722	0.0056	0.0235

Run metric-finetuning on KITTI dataset:

torchrun --nproc_per_node=4 --master_port 4321 train_distill.py \
gpus=[0,1,2,3] num_workers=4 name=DMD3D_BP_KITTI \
++chpt=checkpoints/pretrained_mixed_singleview_256.pth \
net=PMP data=KITTI \
lr=5e-4 train_batch_size=2 test_batch_size=1 \
sched/lr=NoiseOneCycleCosMo sched.lr.policy.max_momentum=0.90 \
nepoch=30 test_epoch=25 ++net.sbn=true

Name		Name	Last commit message	Last commit date
checkpoints		checkpoints
configs		configs
datas		datas
datasets		datasets
exts		exts
models		models
LICENSE		LICENSE
README.md		README.md
augs.py		augs.py
criteria.py		criteria.py
demo.py		demo.py
demo.sh		demo.sh
disp_loss.py		disp_loss.py
environment.yml		environment.yml
optimizers.py		optimizers.py
rpnloss.py		rpnloss.py
run.sh		run.sh
schedulers.py		schedulers.py
test.py		test.py
train_distill.py		train_distill.py
utils.py		utils.py
utils_infer.py		utils_infer.py

Name		Name	Last commit message	Last commit date
checkpoints		checkpoints
configs		configs
datas		datas
datasets		datasets
exts		exts
models		models
LICENSE		LICENSE
README.md		README.md
augs.py		augs.py
criteria.py		criteria.py
demo.py		demo.py
demo.sh		demo.sh
disp_loss.py		disp_loss.py
environment.yml		environment.yml
optimizers.py		optimizers.py
rpnloss.py		rpnloss.py
run.sh		run.sh
schedulers.py		schedulers.py
test.py		test.py
train_distill.py		train_distill.py
utils.py		utils.py
utils_infer.py		utils_infer.py

Name		Name	Last commit message	Last commit date
checkpoints		checkpoints
configs		configs
datas		datas
datasets		datasets
exts		exts
models		models
LICENSE		LICENSE
README.md		README.md
augs.py		augs.py
criteria.py		criteria.py
demo.py		demo.py
demo.sh		demo.sh
disp_loss.py		disp_loss.py
environment.yml		environment.yml
optimizers.py		optimizers.py
rpnloss.py		rpnloss.py
run.sh		run.sh
schedulers.py		schedulers.py
test.py		test.py
train_distill.py		train_distill.py
utils.py		utils.py
utils_infer.py		utils_infer.py

Name		Name	Last commit message	Last commit date
checkpoints		checkpoints
configs		configs
datas		datas
datasets		datasets
exts		exts
models		models
LICENSE		LICENSE
README.md		README.md
augs.py		augs.py
criteria.py		criteria.py
demo.py		demo.py
demo.sh		demo.sh
disp_loss.py		disp_loss.py
environment.yml		environment.yml
optimizers.py		optimizers.py
rpnloss.py		rpnloss.py
run.sh		run.sh
schedulers.py		schedulers.py
test.py		test.py
train_distill.py		train_distill.py
utils.py		utils.py
utils_infer.py		utils_infer.py

Name		Name	Last commit message	Last commit date
checkpoints		checkpoints
configs		configs
datas		datas
datasets		datasets
exts		exts
models		models
LICENSE		LICENSE
README.md		README.md
augs.py		augs.py
criteria.py		criteria.py
demo.py		demo.py
demo.sh		demo.sh
disp_loss.py		disp_loss.py
environment.yml		environment.yml
optimizers.py		optimizers.py
rpnloss.py		rpnloss.py
run.sh		run.sh
schedulers.py		schedulers.py
test.py		test.py
train_distill.py		train_distill.py
utils.py		utils.py
utils_infer.py		utils_infer.py

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DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Important Update!

🆕 Update Log

✅ To Do

🔍 Overview

🚀 Getting Started

1. Clone Base Repository

2. Copy This Repo into the BP-Net Directory

3. Prepare KITTI Raw Data

4. Modify the Sequence in demo.py for Inference

5. Train on KITTI

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DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Important Update!

🆕 Update Log

✅ To Do

🔍 Overview

🚀 Getting Started

1. Clone Base Repository

2. Copy This Repo into the BP-Net Directory

3. Prepare KITTI Raw Data

4. Modify the Sequence in demo.py for Inference

5. Train on KITTI

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DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

Important Update!

🆕 Update Log

✅ To Do

🔍 Overview

🚀 Getting Started

1. Clone Base Repository

2. Copy This Repo into the BP-Net Directory

3. Prepare KITTI Raw Data

4. Modify the Sequence in demo.py for Inference

5. Train on KITTI

About

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DMD³C: Distilling Monocular Foundation Model for Fine-grained Depth Completion

4. Modify the Sequence in `demo.py` for Inference

4. Modify the Sequence in `demo.py` for Inference

4. Modify the Sequence in `demo.py` for Inference

4. Modify the Sequence in `demo.py` for Inference

4. Modify the Sequence in `demo.py` for Inference