Results 101 to 110 of about 26,254 (190)

Increased-Range Unsupervised Monocular Depth Estimation

, 2020
Unsupervised deep learning methods have shown promising performance for single-image depth estimation. Since most of these methods use binocular stereo pairs for self-supervision, the depth range is generally limited. Small-baseline stereo pairs provide small depth range but handle occlusions well.
Imran, Saad, Khan, Muhammad Umar Karim, Mukarram, Sikander Bin, Kyung, Chong-Min   +3 more
openaire   +2 more sources

Mono-ViM: A Self-Supervised Mamba Framework for Monocular Depth Estimation in Endoscopic Scenes

Mathematics
Self-supervised depth estimation methods enable the recovery of scene depth information from monocular endoscopic images, thereby assisting endoscopic navigation.
Shengli Chen, Yuming Chen, Xiaoang Xu, Jiahao Li, Ke Ye, Tianzuo Chang   +5 more
doaj   +1 more source

Depth estimation from monocular images

, 2020
During this project, state-of-the-art deep learning models have been used to estimate depth maps from a monocular RGB image applying a teacher-student learning approach. This paradigm has been used in order to distillate the knowledge of high capacity deep neural networks into shallower ones to make inference faster for real-time applications.
openaire   +1 more source

Deep Neural Networks for Accurate Depth Estimation with Latent Space Features

Biomimetics
Depth estimation plays a pivotal role in advancing human–robot interactions, especially in indoor environments where accurate 3D scene reconstruction is essential for tasks like navigation and object handling.
Siddiqui Muhammad Yasir, Hyunsik Ahn
doaj   +1 more source

Always Clear Depth: Robust Monocular Depth Estimation Under Adverse Weather

Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence
Monocular depth estimation is critical for applications such as autonomous driving and scene reconstruction. While existing methods perform well under normal scenarios, their performance declines in adverse weather, due to challenging domain shifts and difficulties in extracting scene information.
Jiang, Kui, Cao, Jing, Yu, Zhaocheng, Jiang, Junjun, Zhou, Jingchun   +4 more
openaire   +2 more sources

A Unified Framework for Depth-Assisted Monocular Object Pose Estimation

IEEE Access
Monocular Depth Estimation (MDE) and Object Pose Estimation (OPE) are important tasks in visual scene understanding. Traditionally, these challenges have been addressed independently, with separate deep neural networks designed for each task. However, we
Dinh-Cuong Hoang, Phan Xuan Tan, Thu-Uyen Nguyen, Hai-Nam Pham, Chi-Minh Nguyen, Son-Anh Bui, Quang-Tri Duong, van-Duc Vu, van-Thiep Nguyen, van-Hiep Duong, Ngoc-Anh Hoang, Khanh-Toan Phan, Duc-Thanh Tran, Ngoc-Trung Ho, Cong-Trinh Tran   +14 more
doaj   +1 more source

Holography via Monocular Depth Estimation

Proceedings of the International Display Workshops, 2023
Ryota Kibune, Chihiro Tsutake, Keita Takahashi, Toshiaki Fujii   +3 more
openaire   +1 more source

RMTDepth: Retentive Vision Transformer for Enhanced Self-Supervised Monocular Depth Estimation from Oblique UAV Videos

Remote Sensing
Self-supervised monocular depth estimation from oblique UAV videos is crucial for enabling autonomous navigation and large-scale mapping. However, existing self-supervised monocular depth estimation methods face key challenges in UAV oblique video ...
Xinrui Zeng, Bin Luo, Shuo Zhang, Wei Wang, Jun Liu, Xin Su   +5 more
doaj   +1 more source

Language-Based Depth Hints for Monocular Depth Estimation

8 pages, 1 figure.
Auty, Dylan, Mikolajczyk, Krystian
openaire   +2 more sources

Monocular Depth Estimation from a Fisheye Camera Based on Knowledge Distillation. [PDF]

Sensors (Basel), 2023
Son E, Choi J, Song J, Jin Y, Lee SJ.
europepmc   +1 more source