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Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information

ECCV 2024

Given a Structure-from-Motion model, we aim to learn the camera viewpoint that can be employed to maximize the accuracy in visual localization. Our methodology requires first sampling the camera locations and orientation, calculating the best visibility orientation for each location, and learning active viewpoint through a Multi-layer Perceptron encoder. The illustration above shows our full pipeline predicting active viewpoints for visual localization embedded into a planning framework.

arXiv YouTube

Dependencies

The following external dependencies are required

Dependency Version(s) known to work
CUDA <12.1

Important

CUDA is used both during training by torch and to efficiently process viewpoints visibility.

Install (local) via pip

First download this repo and cd learning-where-to-look. Once inside the folder, you can build/install learning-where-to-look using pip

pip install .

Training

Download some data

./download.sh

The script will download some data that you can use to play with:

├── train_MH3D_10_scene --> contains all the preprocessed meshes used for training, SfM models and precomputed viewpoints
├── test_MH3D_2_scene --> contains all the preprocessed meshes used for testing, SfM models and precomputed viewpoints
├── train_data_10_meshes_with_preprocessed_test.pickle --> contains data that can be
|                                                          loaded directly for learning, with essential (observed landmarks 
|                                                          reprojections in image, landmarks in camera frame) train, 
|                                                          validation, test, mean and std (of training set)
├── test_raw_data_2_meshes.pickle --> contains all preprocessed data used 
|                                     for testing with other information (i.e., locations used for evaluation)
└── raw_MH3D_00017.pickle --> preprocessed test mesh you can use to visualize results

Run

Run training with the following script; the default is 300 epochs

python3 lwl/apps/training/mlp_train.py --data_path data/train_data_10_meshes_with_preprocessed_test.pickle --test_data_path data/test_raw_data_2_meshes.pickle --checkpoint_path data/mymodels/tmp_training

Inference

Evaluate the trained model numerically

python3 lwl/apps/inference/evaluate_model.py --train_data_path data/train_data_10_meshes_with_preprocessed_test.pickle --evaluate_data_path data/raw_MH3D_00017.pickle --model_dir data/model/

Evaluate the trained model visually, showing best-predicted viewpoints for each location and their observed landmarks

python3 lwl/apps/inference/compute_active_map.py --train_data_path data/train_data_10_meshes_with_preprocessed_test.pickle --evaluate_data_path data/raw_MH3D_00017.pickle --model_dir data/model/ --enable_viz --config_path configurations/sampler_matterport_1.cfg --landmarks data/test_MH3D_2_scene/MH3D_00017/sparse/0/points3D.txt

Cite us

If you use any of this code, please cite our paper - accepted ECCV 2024:

@article{di2024learning,
  title={Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information},
  author={Di Giammarino, Luca and Sun, Boyang and Grisetti, Giorgio and Pollefeys, Marc and Blum, Hermann and Barath, Daniel},
  journal={arXiv preprint arXiv:2407.15593},
  year={2024}
}

What's Missing

The repo is currently under update; you can keep track of it here

Feature/Component Status
CUDA/C++ compilation ✅ Completed
Unit tests ✅ Completed
Pybidings ✅ Completed
Training ✅ Completed
Documentation ⚠️ In Progress
Preprocessing ⚠️ In Progress
Custom data setup ⚠️ In Progress
Inference/plot active map ✅ In Progress