Omnidepth: Dense depth estimation for indoors spherical panoramas
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 …
360 content which is now increasingly and more easily produced. We show that monocular …
Analyzing CARLA's performance for 2D object detection and monocular depth estimation based on deep learning approaches
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 …
are a common part of the sensor suite. This study explored the use of synthetic datasets from …
Revisiting multi-task learning with rock: a deep residual auxiliary block for visual detection
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 …
paper, we tackle a specific MTL context denoted as primary MTL, where the ultimate goal is …
Uncertainty quantification in depth estimation via constrained ordinal regression
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 …
single image. Despite the recent progress brought by deep learning, existing methods are …
[HTML][HTML] Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar
J Tolan, HI Yang, B Nosarzewski, G Couairon… - Remote Sensing of …, 2024 - Elsevier
Vegetation structure mapping is critical for understanding the global carbon cycle and
monitoring nature-based approaches to climate adaptation and mitigation. Repeated …
monitoring nature-based approaches to climate adaptation and mitigation. Repeated …
Dfinenet: Ego-motion estimation and depth refinement from sparse, noisy depth input with rgb guidance
Depth estimation is an important capability for autonomous vehicles to understand and
reconstruct 3D environments as well as avoid obstacles during the execution. Accurate …
reconstruct 3D environments as well as avoid obstacles during the execution. Accurate …
Monocular depth estimation by learning from heterogeneous datasets
A Gurram, O Urfalioglu, I Halfaoui… - 2018 IEEE Intelligent …, 2018 - ieeexplore.ieee.org
Depth estimation provides essential information to perform autonomous driving and driver
assistance. In particluar, monocular depth estimation is interesting from a practical point of …
assistance. In particluar, monocular depth estimation is interesting from a practical point of …
EfficientNet-B0 Based Monocular Dense-Depth Map Estimation.
Y Tadepalli, M Kollati, S Kuraparthi… - Traitement du …, 2021 - search.ebscohost.com
Monocular depth estimation is a hot research topic in autonomous car driving. Deep
convolution neural networks (DCNN) comprising encoder and decoder with transfer learning …
convolution neural networks (DCNN) comprising encoder and decoder with transfer learning …
Method for training convolutional neural network to reconstruct an image and system for depth map generation from an image
VV Anisimovskiy, AY Shcherbinin, SA Turko - US Patent 10,832,432, 2020 - Google Patents
A method for training a convolutional neural network to reconstruct an image. The method
includes forming a common loss function basing on the left and right images (IL, IR) …
includes forming a common loss function basing on the left and right images (IL, IR) …
AsiANet: Autoencoders in autoencoder for unsupervised monocular depth estimation
JPT Yusiong, PC Naval - 2019 IEEE Winter Conference on …, 2019 - ieeexplore.ieee.org
Monocular depth estimation is extremely challenging because it is inherently an ambiguous
and ill-posed problem. The unsupervised approach to monocular depth estimation using …
and ill-posed problem. The unsupervised approach to monocular depth estimation using …