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Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

Sensors, 2020
Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed problem in computer vision which has been investigated intensively over the past decade using Deep Learning (DL) approaches. The recent approaches for monocular depth
Faisal Khan, Saqib Salahuddin, Hossein Javidnia   +2 more
doaj   +3 more sources

Depth Map Decomposition for Monocular Depth Estimation

Lecture Notes in Computer Science, 2022
We propose a novel algorithm for monocular depth estimation that decomposes a metric depth map into a normalized depth map and scale features. The proposed network is composed of a shared encoder and three decoders, called G-Net, N-Net, and M-Net, which estimate gradient maps, a normalized depth map, and a metric depth map, respectively.
Chul Lee, Jae-Han Lee, Chang-Su Kim
exaly   +4 more sources

RADepthNet: Reflectance-Aware Monocular Depth Estimation

Virtual Reality & Intelligent Hardware, 2022
Background: Monocular depth estimation aims to predict the dense depth map from a single RGB image, which has important applications in 3D reconstruction, automatic driving, and augmented reality.
Chuxuan Li, Ran Yi, Saba Ghazanfar Ali, Lizhuang Ma, Enhua Wu, Jihong Wang, Lijuan Mao, Bin Sheng   +7 more
doaj   +3 more sources

Adversarial Patch Attacks on Monocular Depth Estimation Networks

IEEE Access, 2020
Thanks to the excellent learning capability of deep convolutional neural networks (CNN), monocular depth estimation using CNNs has achieved great success in recent years.
Koichiro Yamanaka, Ryutaroh Matsumoto, Keita Takahashi, Toshiaki Fujii   +3 more
doaj   +3 more sources

Monocular Human Depth Estimation Via Pose Estimation

IEEE Access, 2021
We propose a novel monocular depth estimator, which improves the prediction accuracy on human regions by utilizing pose information. The proposed algorithm consists of two networks — PoseNet and DepthNet — to estimate keypoint heatmaps and ...
Jinyoung Jun, Jae-Han Lee, Chul Lee, Chang-Su Kim   +3 more
doaj   +2 more sources

Survey on Monocular Metric Depth Estimation

Computers
Monocular metric depth estimation (MMDE) aims to generate depth maps with an absolute metric scale from a single RGB image, which enables accurate spatial understanding, 3D reconstruction, and autonomous navigation.
Jiuling Zhang, Yurong Wu, Huilong Jiang
doaj   +4 more sources

Monocular Depth Estimation using Transfer learning-An Overview [PDF]

E3S Web of Conferences, 2021
Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition.
Swaraja K., Akshitha V., Pranav K., Vyshnavi B., Sai Akhil V., Meenakshi K., Kora Padmavathi, Valiveti Himabindu, Duggineni Chaitanya   +8 more
doaj   +1 more source

CNN Based Monocular Depth Estimation [PDF]

E3S Web of Conferences, 2021
In several applications, such as scene interpretation and reconstruction, precise depth measurement from images is a significant challenge. Current depth estimate techniques frequently provide fuzzy, low-resolution estimates.
Swaraja K., Naga Siva Pavan K., Suryakanth Reddy S., Ajay K., Uday Kiran Reddy P., Kora Padmavathi, Meenakshi K., Chaitanya Duggineni, Valiveti Himabindu   +8 more
doaj   +1 more source

EVALUATING MONOCULAR DEPTH ESTIMATION METHODS [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023
Depth estimation from monocular images has become a prominent focus in photogrammetry and computer vision research. Monocular Depth Estimation (MDE), which involves determining depth from a single RGB image, offers numerous advantages, including ...
N. Padkan, N. Padkan, P. Trybala, R. Battisti, F. Remondino, C. Bergeret, C. Bergeret   +6 more
doaj   +1 more source

Lightweight Monocular Depth Estimation

CoRR, 2022
Monocular depth estimation can play an important role in addressing the issue of deriving scene geometry from 2D images. It has been used in a variety of industries, including robots, self-driving cars, scene comprehension, 3D reconstructions, and others.
Ruilin Ma, Shiyao Chen, Qin Zhang
openaire   +3 more sources