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Generating synthetic training datasets

To generate either point-based or image-based training data, we use several similarly structured Python scripts:

Example data generation

Assuming your ABC 7z files are in /path/to/data, code is in /path/to/code, and desired output is in /path/to/outputs you can run the docker image via

cd /path/to/code
docker/run_docker.sh \
  -d /path/to/data \
  -l /path/to/outputs \
  -u

you will see a message produced in the form

**** LAUNCHING CONTAINER artonson/def:latest *******
      Pushing you to /code directory
      Data is at /data
      Writable logs are at /logs
      Environment: PYTHONPATH=/code
      Environment: CUDA_VISIBLE_DEVICES=0,1,2,3
      Exposed ports:
      User in container: --user=1001:1001
groups: cannot find name for group ID 1001

then you can run generation with the following shell code. This will process items from 0th to 100th index in chunk 0 of ABC:

cd /code 

export DATA_PATH_CONTAINER=/data  # path to ABC *obj*.7z and *feat*.7z files
export CODE_PATH_CONTAINER=/code  # path to this repo root
export OUTPUT_PATH_CONTAINER=/logs   # path to outputs and logs
export MAKE_DATA_SCRIPT="${CODE_PATH_CONTAINER}/scripts/data_scripts/generate_depthmap_data.py"
export N_TASKS=4  # process this many items in parallel
export CONFIGS_PATH="${CODE_PATH_CONTAINER}/scripts/data_scripts/configs/depthmap_datasets"  # 
export DATASET_PATH="${CONFIGS_PATH}/high_res_whole.json"
export SLICE_START=0  # item ID to start at (inclusive)
export SLICE_END=100  # item ID to stop at (exclusive)
export CHUNK=0  # which chunk to use 
export VERBOSE_ARG="--verbose"  # whether to use verbose logging

python3 ${MAKE_DATA_SCRIPT} \\
    --input-dir ${DATA_PATH_CONTAINER} \\
    --chunk ${CHUNK} \\
    --output-dir ${OUTPUT_PATH_CONTAINER} \\
    --jobs ${N_TASKS} \\
    -n1 ${SLICE_START} \\
    -n2 ${SLICE_END} \\
    --dataset-config ${DATASET_PATH} \\
     ${VERBOSE_ARG}

Let the script run for a while, and it will produce data and logs (both in console and in the output folder). Running the other data generation scripts is completely similar to running the previous code snippet.

Precomputed datasets (DEF-Sim)

To foster further research, we have precomputed and publish a few datasets as our DEF-Sim data.

Patch-based datasets (training and evaluation)

These datasets were used to train our models. For image-based datasets, the resolution of 64x64 (4096 points, maximum) has been chosen. For point-based datasets, 4096 points are sampled from each patch. We do not release datasets corresponding to high-resolution (r = 0.02) models with noise levels 0.0025, 0.01, and 0.04 to save storage.

The following datasets are available by the synthetic_data/patches/ folder:

Link Modality Resolution Sampling distance Noise level
def_sim-images-high-0-arbitrary-patches-*.tar.gz depth images 64 x 64 0.02 0
def_sim-images-high-0.005-arbitrary-patches-*.tar.gz depth images 64 x 64 0.02 0.005 (SNR=4:1)
def_sim-images-high-0.02-arbitrary-patches-*.tar.gz depth images 64 x 64 0.02 0.02 (SNR=1:1)
def_sim-images-high-0.08-arbitrary-patches-*.tar.gz depth images 64 x 64 0.02 0.08 (SNR=1:4)
def_sim-images-med-0-arbitrary-patches-*.tar.gz depth images 64 x 64 0.05 (2.5x) 0
def_sim-images-low-0-arbitrary-patches-*.tar.gz depth images 64 x 64 0.125 (2.5^2x) 0
def_sim-points-high-0-arbitrary-patches-*.tar.gz point clouds 4096 0.02 0
def_sim-points-high-0.005-arbitrary-patches-*.tar.gz point clouds 4096 0.02 0.005 (SNR=4:1)
def_sim-points-high-0.02-arbitrary-patches-*.tar.gz point clouds 4096 0.02 0.02 (SNR=1:1)
def_sim-points-high-0.08-arbitrary-patches-*.tar.gz point clouds 4096 0.02 0.08 (SNR=1:4)
def_sim-points-med-0-arbitrary-patches-*.tar.gz point clouds 4096 0.05 (2.5x) 0
def_sim-points-low-0-arbitrary-patches-*.tar.gz point clouds 4096 0.125 (2.5^2x) 0

Complete 3D model datasets (intended for evaluation only)

Please note that these shapes are high-resolution, densely sampled point clouds typically with millions of points.

The folder synthetic_data/complete_models/ contains all the files.

Link Modality Resolution Sampling distance Noise level Num. views Num. shapes
def_sim-images-high-0-18views.tar.gz depth images 1024 x 1024 0.02 0 18 85
def_sim-images-high-0-128views.tar.gz depth images 1024 x 1024 0.02 0 128 95
def_sim-images-high-0.005.tar.gz depth images 1024 x 1024 0.02 0.005 (SNR=4:1) 18 87
def_sim-images-high-0.02.tar.gz depth images 1024 x 1024 0.02 0.02 (SNR=1:1) 18 87
def_sim-images-high-0.08.tar.gz depth images 1024 x 1024 0.02 0.08 (SNR=1:4) 18 87
def_sim-images-med.tar.gz depth images 1024 x 1024 0.05 (2.5x) 0 18 100
def_sim-images-low.tar.gz depth images 1024 x 1024 0.125 (2.5^2x) 0 18 104
def_sim-points-high-0.tar.gz point clouds various 0.02 0 point patches 84
def_sim-points-high-0.005.tar.gz point clouds various 0.02 0.005 (SNR=4:1) point patches 83
def_sim-points-high-0.02.tar.gz point clouds various 0.02 0.02 (SNR=1:1) point patches 84
def_sim-points-high-0.08.tar.gz point clouds various 0.02 0.08 (SNR=1:4) point patches 83
def_sim-points-med-0.tar.gz point clouds various 0.05 (2.5x) 0 point patches 103
def_sim-points-low-0.tar.gz point clouds various 0.125 (2.5^2x) 0 point patches 75