Recent work on depth estimation up to now has only focused on projective images ignoring
360 content which is now increasingly and more easily produced. We show that monocular …

Vegetation structure map** is critical for understanding the global carbon cycle and
monitoring nature-based approaches to climate adaptation and mitigation. Repeated …

Vehicle and pedestrian perception are key for autonomous vehicles, and camera images
are a common part of the sensor suite. This study explored the use of synthetic datasets from …

Abstract Multi-Task Learning (MTL) is appealing for deep learning regularization. In this
paper, we tackle a specific MTL context denoted as primary MTL, where the ultimate goal is …

Abstract Monocular Depth Estimation (MDE) is a task to predict a dense depth map from a
single image. Despite the recent progress brought by deep learning, existing methods are …

Depth estimation provides essential information to perform autonomous driving and driver
assistance. In particluar, monocular depth estimation is interesting from a practical point of …

Depth estimation is an important capability for autonomous vehicles to understand and
reconstruct 3D environments as well as avoid obstacles during the execution. Accurate …

Monocular depth estimation is a hot research topic in autonomous car driving. Deep
convolution neural networks (DCNN) comprising encoder and decoder with transfer learning …

US10832432B2 - Method for training convolutional neural network to reconstruct an image and
system for depth map generation from an image - Google Patents US10832432B2 - Method for …

Depth estimation is a critical step for many computer vision tasks such as scene
understanding, registration, and localization. The view synthesis-based method estimates …