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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 626–650 of 876 papers

TitleStatusHype
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth PredictionCode1
Improved Point Transformation Methods For Self-Supervised Depth PredictionCode0
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance—0
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection ConsistencyCode1
Deep Learning--Based Scene Simplification for Bionic VisionCode0
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images—0
Monocular Depth Estimation Using Laplacian Pyramid-Based Depth ResidualsCode1
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction—0
Monocular Depth Estimation for Soft Visuotactile Sensors—0
R-MSFM: Recurrent Multi-Scale Feature Modulation for Monocular Depth EstimatingCode1
Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?—0
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
Monocular Depth Parameterizing NetworksCode0
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth EstimationCode1
Self-supervised monocular depth estimation from oblique UAV videosCode0
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion—0
Learning to Recover 3D Scene Shape from a Single ImageCode2
Detecting Invisible PeopleCode1
HR-Depth: High Resolution Self-Supervised Monocular Depth EstimationCode1
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic SegmentationCode1
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
AdaBins: Depth Estimation using Adaptive BinsCode1
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