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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 276–300 of 876 papers

TitleStatusHype
EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text AlignmentCode1
Excavating the Potential Capacity of Self-Supervised Monocular Depth EstimationCode1
Guiding Monocular Depth Estimation Using Depth-Attention VolumeCode1
Aerial Single-View Depth Completion with Image-Guided Uncertainty EstimationCode1
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised TrainingCode1
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine DomainCode1
Holopix50k: A Large-Scale In-the-wild Stereo Image DatasetCode1
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth EstimationCode1
EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth PredictionCode1
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehiclesCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
IEBins: Iterative Elastic Bins for Monocular Depth EstimationCode1
RCDPT: Radar-Camera fusion Dense Prediction TransformerCode1
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that MatterCode1
Image Masking for Robust Self-Supervised Monocular Depth EstimationCode1
Feature-metric Loss for Self-supervised Learning of Depth and EgomotionCode1
Implicit Integration of Superpixel Segmentation into Fully Convolutional NetworksCode1
Improving Deep Regression with Ordinal EntropyCode1
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised LearningCode1
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth EstimationCode1
RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic ScenesCode1
InSpaceType: Reconsider Space Type in Indoor Monocular Depth EstimationCode1
InSpaceType: Dataset and Benchmark for Reconsidering Cross-Space Type Performance in Indoor Monocular DepthCode1
RePoseD: Efficient Relative Pose Estimation With Known Depth InformationCode1
Single Image Depth Estimation Trained via Depth from Defocus CuesCode1
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