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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 826–850 of 876 papers

TitleStatusHype
MERCI: A NEW METRIC TO EVALUATE THE CORRELATION BETWEEN PREDICTIVE UNCERTAINTY AND TRUE ERROR—0
Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy MapsCode0
Style Augmentation: Data Augmentation via Style RandomizationCode0
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric AnnotationsCode0
Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss—0
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery—0
Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement—0
Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement—0
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation—0
Rethinking Monocular Depth Estimation with Adversarial Training—0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic ImageryCode0
Learning monocular depth estimation with unsupervised trinocular assumptionsCode0
Unsupervised Adversarial Depth Estimation using Cycled Generative NetworksCode0
OmniDepth: Dense Depth Estimation for Indoors Spherical PanoramasCode0
Deep multi-scale architectures for monocular depth estimation—0
Monocular Depth Estimation with Augmented Ordinal Depth Relationships—0
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion SegmentationCode0
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images—0
Adversarial Structure Matching for Structured Prediction TasksCode0
Dual CNN Models for Unsupervised Monocular Depth EstimationCode0
Estimating Depth from RGB and Sparse SensingCode0
Structured Attention Guided Convolutional Neural Fields for Monocular Depth EstimationCode0
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object BoundariesCode0
Monocular Depth Estimation by Learning from Heterogeneous Datasets—0
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