The KITTI Vision Benchmark Suite

The advent of autonomous driving and advanced driver assistance systems necessitates continuous developments in computer vision for 3D scene understanding. Self-supervised monocular depth estimation, a method for pixel-wise distance estimation of objects from a single camera without the use of ground truth labels, is an important task in 3D scene understanding. However, existing methods for this task are limited to convolutional neural network (CNN) architectures. In contrast with CNNs that use localized linear operations and lose feature resolution across the layers, vision transformers process at constant resolution with a global receptive field at every stage. While recent works have compared transformers against their CNN counterparts for tasks such as image classification, no study exists that investigates the impact of using transformers
for self-supervised monocular depth estimation. Here, we first demonstrate how to adapt vision transformers for self-supervised monocula

See paper for details

@conference{mtsfmlearner,
author={Arnav Varma. and Hemang Chawla. and Bahram Zonooz. and Elahe Arani.},
title={Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={758-769},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010884000003124},
isbn={978-989-758-555-5},
}