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JinhwiPark / DepthPrompting

[CVPR24] Depth Prompting for Sensor-Agnostic Depth Estimation
https://www.jinhwipark.com/
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[CVPR2024] Depth Prompting for Sensor-Agnostic Depth Estimation

[Arxiv] [Code] [Video] [Poster] [Slide]

The source code contains

Requirements

Data Preparation

Pretrained Weight and Data Split for NYU & KITTI

Download the weight files and json files at URL

NYU Depth V2 data Preparation

Please download the preprocessed NYU Depth V2 dataset in HDF5 formats provided by Fangchang Ma.

mkdir data; cd data
wget http://datasets.lids.mit.edu/sparse-to-dense/data/nyudepthv2.tar.gz
tar -xvf nyudepthv2.tar.gz && rm -f nyudepthv2.tar.gz
mv nyudepthv2 nyudepth_hdf5

After that, you will get a data structure as follows:

nyudepthv2
├── train
│    ├── basement_0001a
│    │    ├── 00001.h5
│    │    └── ...
│    ├── basement_0001b
│    │    ├── 00001.h5
│    │    └── ...
│    └── ...
└── val
    └── official
        ├── 00001.h5
        └── ...

KITTI data Preparation

Please download the KITTI DC dataset at the KITTI DC Website.

For color images, KITTI Raw dataset is also needed, which is available at the KITTI Raw Website.

Please follow the official instructions (cf., devkit/readme.txt in each dataset) for preparation.

After downloading datasets, you should first copy color images, poses, and calibrations from the KITTI Raw to the KITTI DC dataset.

cd src/utils
python prepare_KITTI_DC.py --path_root_dc PATH_TO_KITTI_DC --path_root_raw PATH_TO_KITTI_RAW

After that, you will get a data structure as follows:

├── depth_selection
│    ├── test_depth_completion_anonymous
│    │    ├── image
│    │    ├── intrinsics
│    │    └── velodyne_raw
│    ├── test_depth_prediction_anonymous
│    │    ├── image
│    │    └── intrinsics
│    └── val_selection_cropped
│        ├── groundtruth_depth
│        ├── image
│        ├── intrinsics
│        └── velodyne_raw
├── train
│    ├── 2011_09_26_drive_0001_sync
│    │    ├── image_02
│    │    │     └── data
│    │    ├── image_03
│    │    │     └── data
│    │    ├── oxts
│    │    │     └── data
│    │    └── proj_depth
│    │        ├── groundtruth
│    │        └── velodyne_raw
│    └── ...
└── val
    ├── 2011_09_26_drive_0002_sync
    └── ...

VOID data Preparation

Please download the VOID dataset at google drive.

https://drive.google.com/open?id=1kZ6ALxCzhQP8Tq1enMyNhjclVNzG8ODA
https://drive.google.com/open?id=1ys5EwYK6i8yvLcln6Av6GwxOhMGb068m
https://drive.google.com/open?id=1bTM5eh9wQ4U8p2ANOGbhZqTvDOddFnlI

which will give you three files void_150.zip, void_500.zip, void_1500.zip

After unzip, create the following data structure

VOID dataset
├── void_150-0
├── void_150-1
├── ...
├── void_500-0
├── void_500-1
├── ...
├── void_1500-0
├── void_1500-1
└── ...

SUN RGBD data Preparation

Please download the SUN RGBD dataset at the SUN RGBD Website

After unzip, create the following data structure

SUNRGBD 
├── kv1
├── kv2
├── realsense
└── xtion

IPAD data Preparation

Save the images and depths taken with the iPad in NPZ format (see this address for more information: HNDR Website)

After the above procedure, create a data structure that looks like this

ipad dataset
├── selected
│    ├── bundle-2023-09-25_15-48-09 
│              └── frame_bundle.npz
│    ├── bundle-2023-09-25_15-48-13
│               └── frame_bundle.npz
│    ├── bundle-2023-09-25_15-48-15
│    ├── ...

nuScenes data Preparation

Please download the NUSCENES dataset at the NUSCENES Website

After unzip, create the following data structure

nuScenes dataset
├── maps
├── samples
├── sweeps
├── v1.0-mini
└── .v1.0-mini.txt

[NYU Depth V2] Training & Testing

# Train
python main_DP.py --data_name NYU --dir_data {Dataset Directory} --gpus 0 --num_sample random --batch_size 1 --model_name depth_prompt_main --save OURS-NYU --patch_height 240 --patch_width 320 --prop_kernel 9 --prop_time 18 --init_scailing --loss L1L2_SILogloss_init2 

# Test
python test_multiDataLoader.py --data_name NYU --dir_data {Dataset Directory} --gpus 0--model_name depth_prompt_main --pretrain {Pretrained weight} --prop_kernel 9 --conf_prop --prop_time 18 --patch_height 240 --patch_width 320 --nyu_val_samples 500,200,100,5,1 --init_scailing

[KITTI Depth Completion] Training & Testing

# Train
python main_DP.py --data_name KITTIDC --dir_data {Dataset Directory} --gpus 0 --top_crop 100 --lidar_lines random_lidar --batch_size 1 --model_name depth_prompt_main --save OURS-KITTI --patch_height 240 --patch_width 1216 --prop_kernel 9 --prop_time 18 --conf_prop --init_scailing --loss L1L2_SILogloss_init 

# Test
python test_multiDataLoader.py --data_name KITTIDC --dir_data {Dataset Directory} --gpu 0 --model_name depth_prompt_main --pretrain {Pretrained weight} --top_crop 0 --kitti_val_lidars 64,32,16,8,4,2,1,0 --prop_kernel 9 --prop_time 18 --conf_prop --init_scailing

[VOID] Testing

# Test 
 python test_multiDataLoader.py --data_name VOID --void_sparsity {choose from 150, 500, 1500} --dir_data {Dataset Directory} --gpus 0 --model_name depth_prompt_main --pretrain {Pretrained weight}  --prop_kernel 9 --conf_prop --prop_time 18 --patch_height 240 --patch_width 320 --init_scailing

[SUN RGBD] Testing

# Test
python test_multiDataLoader.py --data_name SUNRGBD --dir_data {Dataset Directory} --gpus 0 --model_name depth_prompt_main --pretrain {Pretrained weight}  --prop_kernel 9 --conf_prop --prop_time 18 --patch_height 240 --patch_width 320 --init_scailing --use_raw_depth_as_input

[IPAD] Testing (Only visualization)

# Test
python test_multiDataLoader.py --data_name IPAD --dir_data {Dataset Directory} --save_dir {Save Directory} --gpu 0 --model_name depth_prompt_main --pretrain {Pretrained weight} --nyu_val_samples 0,1,5 --patch_height 228 --patch_width 304 --conf_select -v

[nuScenes] Testing (Only visualization)

# Test
python test_multiDataLoader.py --data_name NUSCENE --dir_data {Dataset Directory} --save_dir {Save Directory} --gpu 0 --model_name depth_prompt_main --pretrain {Pretrained weight} --top_crop 0 --kitti_val_lidars 64 --prop_kernel 9 --prop_time 18 --conf_prop --init_scailing -v

Acknowledgement

This code is based on the original implementations: CSPN(paper), NLSPN(paper), GraphCSPN(paper), HNDR(paper)