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Monocular Depth Estimation

Monocular Depth Estimation is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image. This challenging task is a key prerequisite for determining scene understanding for applications such as 3D scene reconstruction, autonomous driving, and AR. State-of-the-art methods usually fall into one of two categories: designing a complex network that is powerful enough to directly regress the depth map, or splitting the input into bins or windows to reduce computational complexity. The most popular benchmarks are the KITTI and NYUv2 datasets. Models are typically evaluated using RMSE or absolute relative error.

Source: Defocus Deblurring Using Dual-Pixel Data

Papers

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Showing 701–725 of 876 papers

TitleStatusHype
Monocular Depth Parameterizing NetworksCode0
Self-supervised monocular depth estimation from oblique UAV videosCode0
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion—0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
Variational Monocular Depth Estimation for Reliability Prediction—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
Distortion-aware Monocular Depth Estimation for Omnidirectional Images—0
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature ExtractionCode0
Monocular Differentiable Rendering for Self-Supervised 3D Object Detection—0
Towards General Purpose Geometry-Preserving Single-View Depth Estimation—0
Calibrating Self-supervised Monocular Depth Estimation—0
Cascade Network for Self-Supervised Monocular Depth Estimation—0
Monocular Depth Estimation Using Multi Scale Neural Network And Feature Fusion—0
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency—0
Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi—0
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP—0
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving—0
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss—0
Disambiguating Monocular Depth Estimation with a Single Transient—0
Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference & Application—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets—0
Mobile3DRecon: Real-time Monocular 3D Reconstruction on a Mobile Phone—0
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