In current decades, significant advancements in robotics engineering and autonomous
vehicles have improved the requirement for precise depth measurements. Depth estimation …

Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene
information from single camera images, which is trainable on arbitrary image sequences …

Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this
limitation, self-supervised learning has emerged as a promising alternative for training …

Self-supervised paradigms for monocular depth estimation are very appealing since they do
not require ground truth annotations at all. Despite the astonishing results yielded by such …

In this paper, we propose Recurrent Multi-Scale Feature Modulation (R-MSFM), a new deep
network architecture for self-supervised monocular depth estimation. R-MSFM extracts per …

As an essential component for many autonomous driving and robotic activities such as ego-
motion estimation, obstacle avoidance and scene understanding, monocular depth …

Trees in urban areas have diverse ecological, social, and health benefits. The establishment
of up-to-date and accurate street-tree inventories that list the species and locations of …

Depth estimation from a single image represents a fascinating, yet challenging problem with
countless applications. Recent works proved that this task could be learned without direct …

Stereo matching is one of the longest-standing problems in computer vision with close to 40
years of studies and research. Throughout the years the paradigm has shifted from local …