Results 81 to 90 of about 3,462,291 (207)

FLSea: Underwater Visual–Inertial and Stereovision Forward‐Looking Data Sets

Journal of Field Robotics, EarlyView.
ABSTRACT Visibility underwater is challenging and degrades as the distance between the subject and the camera increases. That is why forward‐looking underwater computer vision tasks are difficult. We have collected underwater forward‐looking stereovision and visual–inertial image sets using two underwater imaging platforms, a stereo camera rig, and an ...
Yelena Randall, Ori Lifschitz, Tali Treibitz   +2 more
wiley   +1 more source

Towards Explainability in Monocular Depth Estimation

VDC_RelSize The Visual Depth Cue Dataset (VDC) is a synthetic image dataset which focuses on monocular depth estimation tasks. VDC is inspired by the research of Cutting & Vishton about the human depth perception system. Relative Size Τhe real world is perceived with perspective, which causes objects that are closer to the observer to appear larger ...
Arampatzakis, Vasileios, Pavlidis, George, Pantoglou, Kyriakos, Mitianoudis, Nikolaos, Papamarkos, Nikos   +4 more
openaire   +4 more sources

Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior [PDF]

2019 IEEE International Conference on Image Processing (ICIP), 2019
Monocular depth estimation using novel learning-based approaches has recently emerged as a promising potential alternative to more conventional 3D scene capture technologies within real-world scenarios. Many such solutions often depend on large quantities of ground truth depth data, which is rare and often intractable to obtain.
Amir Atapour Abarghouei, Toby P. Breckon
openaire   +3 more sources

Formation Control With Obstacle Avoidance of Underwater Swarms Based on Relative Visual Feedback

Journal of Field Robotics, EarlyView.
ABSTRACT Underwater multi‐robot and swarm systems require advanced methodologies for controlling collective behavior. Conventional single‐robot techniques, such as tethering, sonar imaging, and acoustic localization, are not scalable or effective for swarm applications.
Andrea Infanti, Saverio Iacoponi, Saif Alameri, Mohamed El Hanbaly, Mohammed Tarnini, Cesare Stefanini, Giulia De Masi, Federico Renda   +7 more
wiley   +1 more source

Scale Recovery for Monocular Depth Estimation: a Semantic Segmentation-based Approach

, 2023
reservedIn un era contrassegnata dalla rapida integrazione dell’intelligenza artificiale e della robotica, l’interesse nella navigazione autonoma nei robot mobili è emerso come frontiera critica di ricerca.
SARRAGGIOTTO, DAVIDE
core  

Monocular 3D Object Detection Based on Height-Depth Constraint and Edge Fusion [PDF]

Jisuanji kexue
Monocular 3D object detection aims to complete 3D object detection using monocular images,and most existing monocular 3D object detection algorithms are based on classical 2D object detection algorithms.To address the issue of inaccurate instance depth ...
PU Bin, LIANG Zhengyou, SUN Yu
doaj   +1 more source

ADAPT: An Autonomous Forklift for Construction Site Operation

Journal of Field Robotics, EarlyView.
ABSTRACT Efficient material logistics are critical in controlling costs and schedules in the construction industry. However, manual material handling remains prone to inefficiencies, delays, and safety risks. Autonomous forklifts offer a solution to streamline on‐site logistics, reducing reliance on human operators and mitigating labor shortages.
Johannes Huemer, Markus Murschitz, Matthias Schörghuber, Lukas Reisinger, Thomas Kadiofsky, Christoph Weidinger, Mario Niedermeyer, Benedikt Widy, Marcel Zeilinger, Csaba Beleznai, Tobias Glück, Andreas Kugi, Patrik Zips   +12 more
wiley   +1 more source

Guiding Monocular Depth Estimation Using Depth-Attention Volume [PDF]

, 2020
Recovering the scene depth from a single image is an ill-posed problem that requires additional priors, often referred to as monocular depth cues, to disambiguate different 3D interpretations. In recent works, those priors have been learned in an end-to-end manner from large datasets by using deep neural networks.
Lam Huynh, Phong Nguyen-Ha 0001, Jiri Matas, Esa Rahtu, Janne Heikkilä   +4 more
openaire   +5 more sources

Geo-Temporal Selective Approach for Dynamic Depth Estimation in Outdoor Object Detection and Distance Measurement

IEEE Access
Accurate depth information is crucial for various computer vision applications such as augmented reality, 3D modeling, and autonomous vehicles. Recent advancements have significantly improved both supervised and self-supervised methods for depth ...
Muhammad Faseeh, Misbah Bibi, Murad Ali Khan, Atif Rizwan, Rashid Ahmad, Do Hyeun Kim   +5 more
doaj   +1 more source

VDNeRF: Vision‐Only Dynamic Neural Radiance Field for Urban Scenes

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Neural radiance fields (NeRFs) implicitly model continuous three‐dimensional scenes using a set of images with known camera poses, enabling the rendering of photorealistic novel views. However, existing NeRF‐based methods encounter challenges in applications such as autonomous driving and robotic perception, primarily due to the difficulty of ...
Zhengyu Zou, Jingfeng Li, Hao Li, Xiaolei Hou, Jinwen Hu, Jingkun Chen, Lechao Cheng, Dingwen Zhang   +7 more
wiley   +1 more source