SALVe: Semantic Alignment Verification for Floorplan Reconstruction from Sparse Panoramas (ECCV 2022, Official Repo)
[PDF] [Supp. Mat]
John Lambert, Yuguang Li, Ivaylo Boyadzhiev, Lambert Wixson, Manjunath Narayana, Will Hutchcroft, James Hays, Frank Dellaert, Sing Bing Kang.
Presented at ECCV 2022. Link to SALVe video (5 min).

Repository Structure

Code is organized under the salve directory as follows:

salve:
- algorithms: graph and SfM related algorithms, e.g. rotation averaging, Pose(2) SLAM, spanning tree computation, cycle consistency
- baselines: wrappers around other SfM implementations (OpenSfM, OpenMVG) that we use as baseline comparisons.
- common: shared classes for room layout data, W/D/O objects, and 2d pose graphs
- configs: Hydra config files for model training
- dataset: dataloaders
- models: CNN model architectures, implemented in Pytorch
- stitching: room layout stitching
- utils: utilities for rasterization, rendering graphs, Sim(3) alignment, precision/recall computation, and more.

Installation

To install the salve library, clone the repository and run:

git clone https://github.com/johnwlambert/salve.git
cd salve/
pip install -e .

Set SALVE_REPO_DIRPATH to wherever you have cloned the salve repo.

Dependencies

We use Conda to manage dependencies. Please install the environment on Linux using:

conda env create -f environment_ubuntu-latest.yml

or on Mac

conda env create -f environment_macos-latest.yml

Notes about libraries used

We use Facebook's hydra library for configuration files.
We use the rdp library for polygon simplification.
We use the GTSAM library for back-end SLAM/pose graph optimization.
We use the GTSFM library for some geometry-related functionality, including pose graph alignment.

Note: Hydra, rdp, GTSAM and GTSFM are all included in the Conda environment.

Verify Installation

Run the unit tests:

pytest tests/

Download ZInD

git clone https://github.com/zillow/zind.git
cd zind
python download_data.py --num_process 10 \
    --output_folder {SAVE_DIR} \
    --server_token {BRIDGE_API_SERVER_TOKEN} \
    --verbose

For example, SAVE_DIR could be /mnt/data/johnlam/zind_bridgeapi_2021_10_05, and BRIDGE_API_SERVER_TOKEN could be a 32-character alphanumeric sequence. More instructions can be found in the ZInD Github repo.

Running Reconstruction

Make sure you are within the conda environment (salve-v1).

Download ModifiedHorizonNet (MHNet) predictions. Download and unzip the custom ModifiedHorizonNet predictions from Google Drive here.

unzip {PATH_TO_HORIZONNET PREDS ZIP FILE}/ZInD_HorizonNet_predictions.tar.gz
ZILLOW_HORIZON_NET_ZIND_PREDICTIONS_DIRPATH = {}

Vanishing angle file Extraction. Run the following command:

python scripts/split_vanishing_angle_file.py --csv assets/zind_vanishing_angles.csv --out {PATH_TO_PREDICTION_DIRECTORY}/vanishing_angle

Generate alignment hypotheses

Run SALVe model inference by first generating alignment hypotheses:

python scripts/export_alignment_hypotheses.py \
    --num_processes {NUM. DESIRED PROCS.} \
    --raw_dataset_dir {PATH TO ZIND} \
    --hypotheses_save_root {DIRECTORY WHERE TO DUMP OUTPUT} \
    --wdo_source horizon_net \
    --split test \
    --mhnet_predictions_data_root {DIRECTORY TO MHNET PREDS} \
     2>&1 | tee alignment_hypotheses_generation_output.log

Using 20-30 processes is recommended, and even with 30 processes, the generation may take 1-2 hours to complete.

Replace --split test with train or val if desired.

Generate depth maps with HoHoNet

To run HoHoNet inference, clone the HoHoNet repo:

cd ..
git clone https://github.com/sunset1995/HoHoNet.git
cd HoHoNet

Now, download the HoHoNet model by executing:

./{SALVE_REPO_DIRPATH}/scripts/download_monodepth_model.sh

You should now see a model checkpoint at:

ls -ltrh HoHoNet/ckpt/mp3d_depth_HOHO_depth_dct_efficienthc_TransEn1_hardnet/ep60.pth

From within the HoHoNet dir, execute:

python {SALVE_REPO_DIRPATH}/scripts/batch_hohonet_inference.py \
    --raw_dataset_dir {PATH TO ZIND} \
    --depth_save_root {PATH TO SAVE DEPTH MAPS} \
    --num_processes {NUM GPU PROCESSES BASED ON GPU RAM}

Each process will likely consume around 4.3 GB of GPU RAM.

Render BEV texture maps.

Run:

export PYTHONPATH=./
python {SALVE_REPO_DIRPATH}/scripts/render_dataset_bev.py \
    --num_processes {NUM. DESIRED PROCESSES} \
    --raw_dataset_dir {PATH TO ZIND} \
    --hypotheses_save_root {PATH TO PRE-GENERATED ALIGNMENT HYPOTHESES} \
    --depth_save_root {PATH TO WHERE DEPTH MAPS WILL BE OR HAVE BEEN SAVED TO} \
    --bev_save_root {PATH WHERE BEV TEXTURE MAPS WILL BE SAVED TO} \
    --split test

Replace --split test with train or val if desired. If you wish to test out SALVe on only a single building, then instead of using the --split CLI arg, use the --building_id arg instead, e.g. --building_id 0001.

If you see an error message like:

HoHoNet's lib could not be loaded, skipping...
Exception:  No module named 'lib'

then you have not configured HoHoNet properly above.

Run SALVe Verifier Inference

Next, we'll send the pairs of BEV texture maps to the model for scoring. You should have pass the folder where the model checkpoint is stored as model_save_dirpath. You can create this yourself (if performing inference with a pretrained model), or it would be automatically created during training.

python scripts/test.py \
    --gpu_ids {COMMA SEPARATED GPU ID LIST} \
    --model_ckpt_fpath {PATH TO TRAINED PYTORCH MODEL CHECKPOINT} \
    --config_name {FILE NAME OF YAML MODEL CONFIG} \
    --serialization_save_dir {PATH WHERE SERIALIZED PREDS WILL BE SAVED TO}

Please note that the YAML config must be the name of a config under salve/configs/*.

For example, if your model checkpoint was stored in a directory accessible at:

/data/johnlambert/models/2021_11_19_21_42_11

and contained pytorch model checkpoint (.pth) train_ckpt.pth, then we would use via CLI

--model_ckpt_fpath /data/johnlambert/models/2021_11_19_21_42_11/train_ckpt.pth

In the config you'll see a line:

    data_root: /data/johnlam/ZinD_Bridge_API_BEV_2021_10_20_lowres

this should be replaced with bev_save_root where renderings were saved to, above. When running scripts/test.py, the YAML’s model_save_dirpath will be ignored, since this is only used at training time.

Please note that serialization_save_dir is a new directory created at inference time, where predictions on the val or test set will be cached as JSON.

Run Global SfM

Now, pass the front-end measurements to SfM:

python scripts/run_sfm.py \
     --raw_dataset_dir {PATH TO ZIND} \
     --method pgo \
     --serialized_preds_json_dir {PATH TO WHERE SERIALIZED PREDS WERE SAVED TO} \
     --hypotheses_save_root {PATH TO PRE-GENERATED ALIGNMENT HYPOTHESES}

Above, we use pose-graph optimization (pgo) as the global aggregation method for relative poses.

Pretrained Models

We release 7 pretrained models:

W/D/O + Layout Source	Input Modalities	#Tours used for training	Arch.	Model filename (md5sum)
MHNet	Ceiling + Floor RGB	435	ResNet-152	1200ffbe47d836557d88fef052952337.pth
MHNet	Ceiling + Floor RGB	587	ResNet-152	9fcbb628bd5efffbdcc4ce55a9eb380d.pth
MHNet	Ceiling RGB only	587	ResNet-152	5c64123c134b829dd99beb3684582f61.pth
MHNet	Floor RGB only	587	ResNet-152	a063532031f83aec97289466943bf52d.pth
MHNet	Rasterized Layout (Floor)	877	ResNet-152	6ac3f3e5fe6fa3d4bfae7c124d7787b3.pth
GT W/D/O + GT Layout	Ceiling + Floor RGB	350	ResNet-152	301f920ec795b9966aebc2367544d234.pth
GT W/D/O + GT Layout	Ceiling + Floor RGB	817	ResNet-152	b1198bad27aecb8a19f884abc920a731.pth

Training a model

python scripts/train.py \
    --gpu_ids {COMMA SEPARATED GPU ID LIST} \
    --config_name {FILE NAME OF YAML CONFIG under salve/configs/}

A directory will be created which contains the config file used for training (yaml), and a pytorch model checkpoint (.pth), and JSON results, e.g.:

 train_ckpt.pth
 results-2021_11_19_21_42_11-2021_11_09_resnet152_ceiling_floor_rgbonly_no_photometric_augment.json
 2021_11_09_resnet152_ceiling_floor_rgbonly_no_photometric_augment.yaml

Floor Map Stitching

python scripts/stitch_floor_plan.py --output-dir output_folder \
    --path-clusters ../tests/test_data/example_input_stiching/cluster_pred.json \
    --pred-dir ../tests/test_data/example_input_stiching/pano/ \
    --path-gt ../tests/test_data/example_input_stiching/floor_map_gt.json

Run Static Analysis of Codebase

flake8 --max-line-length 120 --ignore E201,E202,E203,E231,W291,W293,E303,W391,E402,W503,E731 salve

pytest tests --cov salve
coverage report

Running SfM Baselines

In the paper, we compare with OpenMVG and OpenSfM. You can run these baselines yourself as follows.

OpenSfM: First, clone the repository:

git clone --recursive https://github.com/mapillary/OpenSfM
cd OpenSfM

Now, build the library/binaries, as described here.

Next, inside the OpenSfM directory, now run:

python {SALVE_REPO_DIRPATH}/salve/scripts/execute_opensfm.py \
   --raw_dataset_dir {PATH_TO_ZIND} \
   --opensfm_repo_root {TODO} \
   --overrides_fpath {TODO}

Next, evaluate the OpenSfM results:

python {SALVE_REPO_DIRPATH}/scripts/evaluate_sfm_baseline.py \
    --algorithm_name opensfm \
    --raw_dataset_dir {PATH_TO_ZIND} \
    --results_dir {PATH TO WHERE OPENSFM SCRIPT DUMPED RECONSTRUCTION RESULTS} \
    --save_dir {WHERE TO SAVE VISUALIZATIONS/RESULT SUMMARIES }

OpenMVG:

Follow the instructions here on how to clone and build the repository.

After compilation, run the following script:

python scripts/execute_openmvg.py \
    --raw_dataset_dir {PATH_TO_ZIND} \
    --openmvg_sfm_bin {PATH_TO_DIRECTORY_CONTAINING_COMPILED_BINARIES} \
    --openmvg_demo_root {TODO}

Evaluate the OpenMVG results using:

python {SALVE_REPO_DIRPATH}/scripts/evaluate_sfm_baseline.py \
    --algorithm_name openmvg \
    --raw_dataset_dir {PATH_TO_ZIND} \
    --results_dir {PATH TO WHERE OPENMVG SCRIPT DUMPED RECONSTRUCTION RESULTS} \
    --save_dir {WHERE TO SAVE VISUALIZATIONS/RESULT SUMMARIES }

FAQ

Q: For Open3d dependencies, I see OSError: /lib/x86_64-linux-gnu/libm.so.6: version GLIBC_2.27' not found` upon import?

A: You are on Ubuntu <=16.04, but you should be on Ubuntu >=18.04 (see here).

Q: How can I see some examples of ZInD's annotated floor plans?

A: python scripts/viz_zind_annotated_floorplans

Q: How was the Modified HorizonNet (MHNet) trained? How does it differ from HorizonNet?

A: This model was trained on a combination of ZInD data and data from additional furnished homes. Its outputs differ from HorizonNet. For legal reasons, we cannot release this dataset. We will not release the MHNet model weights, but we do provide the inference results on ZInD. See horizon_net_schema.json for the file format of predictions (one JSON file per panorama).

Q: How are vanishing points used in SALVe?

A: SALVe generates relative pose hypotheses for a given pair of panoramas by snapping the mid-points of identified doors or windows pairs from the predicted room shape of each panorama. The snapping is done in xy direction (horizontal plane), with the relative yaw angle defined by snapping the opposite outward normals of the door / window line segments. The computed panorama yaw angle is then refined by aligning the horizontal vanishing points of 2 panoramas.

Q: How can I visualize a loss plot, given a training log (JSON file) generated during training?

A: Run python scripts/visualize_loss_plot.py.

zillow/salve