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MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric Learning [PDF]

Deep metric learning aims to learn features relying on the consistency or divergence of class labels. However, in monocular depth estimation, the absence of a natural definition of class poses challenges in the leveraging of deep metric learning ...
Yang, Guanglei, Zuo, Wangmeng, Liu, Chunpu, Zan, Tianyi   +3 more
core   +1 more source

Deep Learning-Based Stereopsis and Monocular Depth Estimation Techniques: A Review

Vehicles
A lot of research has been conducted in recent years on stereo depth estimation techniques, taking the traditional approach to a new level such that it is in an appreciably good form for competing in the depth estimation market with other methods ...
Somnath Lahiri, Jing Ren, Xianke Lin
doaj   +1 more source

Breaking New Ground in Monocular Depth Estimation with Dynamic Iterative Refinement and Scale Consistency

Applied Sciences
Monocular depth estimation (MDE) is a critical task in computer vision with applications in autonomous driving, robotics, and augmented reality. However, predicting depth from a single image poses significant challenges, especially in dynamic scenes ...
Akmalbek Abdusalomov, Sabina Umirzakova, Makhkamov Bakhtiyor Shukhratovich, Azamat Kakhorov, Young-Im Cho   +4 more
doaj   +1 more source

Align3R: Aligned Monocular Depth Estimation for Dynamic Videos

Recent developments in monocular depth estimation methods enable high-quality depth estimation of single-view images but fail to estimate consistent video depth across different frames.
Wang, Wenping, Yeung, Sai-Kit, Liu, Yuan, Dou, Zhiyang, Lu, Jiahao, Li, Peng, Lin, Cheng, Cui, Zhiming, Dong, Zhen, Huang, Tianyu   +9 more
core   +3 more sources

Disambiguating Monocular Depth Estimation with a Single Transient

, 2020
Monocular depth estimation algorithms successfully predict the relative depth order of objects in a scene. However, because of the fundamental scale ambiguity associated with monocular images, these algorithms fail at correctly predicting true metric ...
Christopher Metzler, Nishimura, Mark, David B. Lindell, Wetzstein, Gordon, Gordon Wetzstein, Lindell, David B., Mark Nishimura, Metzler, Christopher A.   +7 more
core   +1 more source

Language-Based Depth Hints for Monocular Depth Estimation

CoRR
8 pages, 1 figure.
Dylan Auty, Krystian Mikolajczyk
openaire   +3 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

Sparse Depth Meets Monocular Depth Estimation: A Guided Diffusion Framework for Zero-Shot Depth Completion

openDepth completion involves predicting a dense depth map from sparse depth measurements and a synchronized RGB image. Traditional deep learning-based methods solving this task often lack flexibility and struggle with generalization, especially in zero ...
VIOLA, MASSIMILIANO
core  

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

Thermal Image-to-LiDAR Depth Transformation via Pretrained Visual Model and Two-Stage Depth Refinement

Photonics
LiDAR sensors provide reliable physical distance measurements using laser signals, enabling accurate acquisition of 3D information for various optical systems.
HeeJeong Yoo, Hoon Yoo
doaj   +1 more source