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Multiscale Attention Fusion for Depth Map Super-Resolution Generative Adversarial Networks [PDF]
Entropy, 2023 Color images have long been used as an important supplementary information to guide the super-resolution of depth maps. However, how to quantitatively measure the guiding effect of color images on depth maps has always been a neglected issue.
Dan Xu, Xiaopeng Fan, Wen Gao
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Color-Guided Depth Map Super-Resolution Using a Dual-Branch Multi-Scale Residual Network with Channel Interaction [PDF]
Sensors, 2020 We designed an end-to-end dual-branch residual network architecture that inputs a low-resolution (LR) depth map and a corresponding high-resolution (HR) color image separately into the two branches, and outputs an HR depth map through a multi-scale ...
Ruijin Chen, Wei Gao
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Depth Map Upsampling via Multi-Modal Generative Adversarial Network [PDF]
Sensors, 2019 Autonomous robots for smart homes and smart cities mostly require depth perception in order to interact with their environments. However, depth maps are usually captured in a lower resolution as compared to RGB color images due to the inherent ...
Daniel Stanley Tan +4 more
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Depth Estimation Based on Scene Object Attention and Depth Map Fusion [PDF]
Jisuanji gongcheng, 2023 The existing monocular depth estimation algorithm mainly obtains stereo information from a single image.This approach leads to blurred details of adjacent depth edges and apparent missing objects.A monocular depth estimation algorithm based on scene ...
WEN Jing, YANG Jie
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Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion
IEEE Access, 2021 Monocular depth estimation is a basic task in machine vision. In recent years, the performance of monocular depth estimation has been greatly improved. However, most depth estimation networks are based on a very deep network to extract features that lead
Xin Yang +4 more
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Depth Map Refinement for Immersive Video
IEEE Access, 2021 In this article, we propose a depth map refinement method that increases the quality of immersive video. The proposal highly enhances the inter-view consistency of depth maps (estimated or acquired by any method), crucial for achieving the required ...
Dawid Mieloch +2 more
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Depth Map Super-Resolution Using Guided Deformable Convolution
IEEE Access, 2021 Depth maps acquired by low-cost sensors have low spatial resolution, which restricts their usefulness in many image processing and computer vision tasks.
Joon-Yeon Kim +4 more
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Depth Map Super-Resolution via Cascaded Transformers Guidance
Frontiers in Signal Processing, 2022 Depth information captured by affordable depth sensors is characterized by low spatial resolution, which limits potential applications. Several methods have recently been proposed for guided super-resolution of depth maps using convolutional neural ...
Ido Ariav, Israel Cohen
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Enhancing Focus Volume through Perceptual Focus Factor in Shape-from-Focus
Mathematics, 2023 Shape From Focus (SFF) reconstructs a scene’s shape using a series of images with varied focus settings. However, the effectiveness of SFF largely depends on the Focus Measure (FM) used, which is prone to noise-induced inaccuracies in focus values.
Khurram Ashfaq, Muhammad Tariq Mahmood
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FastMDE: A Fast CNN Architecture for Monocular Depth Estimation at High Resolution
IEEE Access, 2022 A depth map helps robots and autonomous vehicles (AVs) visualize the three-dimensional world to navigate and localize neighboring obstacles. However, it is difficult to develop a deep learning model that can estimate the depth map from a single image in ...
Thien-Thanh Dao +2 more
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