Download the demo scene out of ios-logger from here: https://github.com/zju3dv/NeuralRecon/blob/master/DEMO.md
Follow the instructions in the NeuralRecon repo for how to use ios-logger to make your own captures.
Unzip the folder into your arkit dataset path so that it looks somthing like this:
dataset_path
scans
neucon_demodata_b5f1
...
....
Run the extraction script that uses modified versions of the functions provided by the NeuralRecon authors:
python ./data_scripts/ios_logger_preprocessing.py --data_config configs/data/neucon_arkit_default.yaml
Make sure you set your correct dataset_path
folder.
Run tuple file generation (we've already computed one for you in data_splits):
python ./data_scripts/generate_test_tuples.py --num_workers 16 --data_config configs/data/neucon_arkit_default.yaml
Then run the model using this config file, see the full readme for more.
There is unfortunately a break in the pose in the NR demo scene, so you'll to trim the first 350 frames using --skip_to_frame 350
when running dense frames and --skip_to_frame 83
when running default.
Run:
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/neucon_arkit_default.yaml \
--num_workers 8 \
--batch_size 2 \
--fast_cost_volume \
--run_fusion \
--depth_fuser open3d \
--fuse_color \
--skip_to_frame 83;
CUDA_VISIBLE_DEVICES=0 python test.py --name HERO_MODEL \
--output_base_path OUTPUT_PATH \
--config_file configs/models/hero_model.yaml \
--load_weights_from_checkpoint weights/hero_model.ckpt \
--data_config configs/data/neucon_arkit_dense.yaml \
--num_workers 8 \
--batch_size 2 \
--fast_cost_volume \
--run_fusion \
--depth_fuser open3d \
--fuse_color \
--skip_to_frame 350;
Should get an output that looks like this for default frames: