Results 31 to 40 of about 1,194,643 (283)
Depth Map Decomposition for Monocular Depth Estimation
, 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.
Jinyoung Jun +3 more
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DTS-Depth: Real-Time Single-Image Depth Estimation Using Depth-to-Space Image Construction
Sensors, 2022 As most of the recent high-resolution depth-estimation algorithms are computationally so expensive that they cannot work in real time, the common solution is using a low-resolution input image to reduce the computational complexity.
Hatem Ibrahem +2 more
doaj +1 more source
SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion [PDF]
, 2017 Active depth cameras suffer from several limitations, which cause incomplete and noisy depth maps, and may consequently affect the performance of RGB-D Odometry.
Gao, Yang, Proença, Pedro F.
core +2 more sources
Video Depth Estimation by Fusing Flow-to-Depth Proposals [PDF]
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 Depth from a monocular video can enable billions of devices and robots with a single camera to see the world in 3D. In this paper, we present an approach with a differentiable flow-to-depth layer for video depth estimation. The model consists of a flow-to-depth layer, a camera pose refinement module, and a depth fusion network.
Jiaxin Xie +4 more
openaire +2 more sources
Zoom motion estimation for color and depth videos using depth information
EURASIP Journal on Image and Video Processing, 2020 In this paper, two methods of zoom motion estimation for color and depth videos by using depth information are proposed. Color and depth videos are independently estimated for zoom motion. Zoom for color video is scaled by spatial domain, and depth video
Soon-kak Kwon, Dong-seok Lee
doaj +1 more source
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture [PDF]
, 2018 Deep neural networks are applied to a wide range of problems in recent years. In this work, Convolutional Neural Network (CNN) is applied to the problem of determining the depth from a single camera image (monocular depth).
Bazrafkan, S. +3 more
core +2 more sources
A Review of Benchmark Datasets and Training Loss Functions in Neural Depth Estimation
IEEE Access, 2021 In many applications, such as robotic perception, scene understanding, augmented reality, 3D reconstruction, and medical image analysis, depth from images is a fundamentally ill-posed problem. The success of depth estimation models relies on assembling a
Faisal Khan +4 more
doaj +1 more source
Evaluation of CNN-based Single-Image Depth Estimation Methods [PDF]
, 2018 While an increasing interest in deep models for single-image depth estimation methods can be observed, established schemes for their evaluation are still limited.
A Saxena +6 more
core +2 more sources
Multi-Sensor Fusion Self-Supervised Deep Odometry and Depth Estimation
Remote Sensing, 2022 This paper presents a new deep visual-inertial odometry and depth estimation framework for improving the accuracy of depth estimation and ego-motion from image sequences and inertial measurement unit (IMU) raw data.
Yingcai Wan +4 more
doaj +1 more source
Depth-Relative Self Attention for Monocular Depth Estimation
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023 Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image. To overcome the limitation, deep neural networks rely on various visual hints such as size, shade, and texture extracted from RGB information. However, we observe that if such hints are overly exploited, the network can be biased on RGB
Kyuhong Shim +3 more
openaire +2 more sources