Benefits of Synthetically Pre-Trained Depth-Prediction Networks for Indoor/Outdoor Image Classification

Kelly X. Lin, Irene Cho, Amey Walimbe, Bryan A. Zamora, Alex Rich, Sirius Z. Zhang, Tobias Höllerer; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2023, pp. 360-369

Abstract


Ground truth depth information is necessary for many computer vision tasks. Collecting this information is challenging, especially for outdoor scenes. In this work, we propose utilizing single-view depth prediction neural networks pre-trained on synthetic scenes to generate relative depth, which we call pseudo-depth. This approach is a less expensive option as the pre-trained neural network obtains accurate depth information from synthetic scenes, which does not require any expensive sensor equipment and takes less time. We measure the usefulness of pseudo-depth from pre-trained neural networks by training indoor/outdoor binary classifiers with and without it. We also compare the difference in accuracy between using pseudo-depth and ground truth depth. We experimentally show that adding pseudo depth to training achieves a 4.4% performance boost over the non-depth baseline model on DIODE, a large standard test dataset, retaining 63.8% of the performance boost achieved from training a classifier on RGB and ground truth depth. It also boosts performance by 1.3% on another dataset, SUN397, for which ground truth depth is not available. Our result shows that it is possible to take information obtained from a model pre-trained on synthetic scenes and successfully apply it beyond the synthetic domain to real-world data.

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[bibtex]
@InProceedings{Lin_2023_WACV, author = {Lin, Kelly X. and Cho, Irene and Walimbe, Amey and Zamora, Bryan A. and Rich, Alex and Zhang, Sirius Z. and H\"ollerer, Tobias}, title = {Benefits of Synthetically Pre-Trained Depth-Prediction Networks for Indoor/Outdoor Image Classification}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops}, month = {January}, year = {2023}, pages = {360-369} }