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

• Main Authors: Lin, Kelly X., Cho, Irene, Walimbe, Amey, Zamora, Bryan A., Rich, Alex, Zhang, Sirius Z., Hollerer, Tobias
• Format: Conference Proceeding
• Language: English
• Subjects: Computational modeling; Computer vision; Conferences; Data models; Neural networks; Task analysis; Training
• ISSN: 2690-621X
• DOI: 10.1109/WACVW58289.2023.00040

Ground truth depth information is necessary for many computer vision tasks. Collecting this information is chal-lenging, 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 expen-sive option as the pre-trained neural network obtains ac-curate 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 stan-dard 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 avail-able. 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.