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 …

The success of monocular depth estimation relies on large and diverse training sets. Due to
the challenges associated with acquiring dense ground-truth depth across different …

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 …

Monocular depth estimation has become one of the most studied applications in computer
vision, where the most accurate approaches are based on fully supervised learning models …

A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to
drive towards a desired destination. Today, there are different paradigms addressing the …

Monocular depth estimators can be trained with various forms of self-supervision from
binocular-stereo data to circumvent the need for high-quality laser-scans or other ground …

In this paper, we propose the convolutional spatial propagation network (CSPN) and
demonstrate its effectiveness for various depth estimation tasks. CSPN is a simple and …

We propose a method to learn 3D deformable object categories from raw single-view
images, without external supervision. The method is based on an autoencoder that factors …