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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 251–300 of 876 papers

TitleStatusHype
Learning Stereo from Single ImagesCode1
High Quality Monocular Depth Estimation via Transfer LearningCode1
Unsupervised Monocular Depth Learning in Dynamic ScenesCode1
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular VideoCode1
Lightweight Monocular Depth Estimation through Guided DecodingCode1
Instance-wise Depth and Motion Learning from Monocular VideosCode1
ENRICH: Multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetryCode1
InSpaceType: Dataset and Benchmark for Reconsidering Cross-Space Type Performance in Indoor Monocular DepthCode1
CutDepth:Edge-aware Data Augmentation in Depth EstimationCode1
Improving Deep Regression with Ordinal EntropyCode1
Attention Attention Everywhere: Monocular Depth Prediction with Skip AttentionCode1
Implicit Integration of Superpixel Segmentation into Fully Convolutional NetworksCode1
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its UncertaintyCode1
Cross-modal transformers for infrared and visible image fusionCode1
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised LearningCode1
Atlantis: Enabling Underwater Depth Estimation with Stable DiffusionCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
IEBins: Iterative Elastic Bins for Monocular Depth EstimationCode1
InSpaceType: Reconsider Space Type in Indoor Monocular Depth EstimationCode1
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised TrainingCode1
Global and Hierarchical Geometry Consistency Priors for Few-shot NeRFs in Indoor ScenesCode1
DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation ModelCode1
LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic SegmentationCode1
DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth AdaptationCode1
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
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection ConsistencyCode1
HR-Depth: High Resolution Self-Supervised Monocular Depth EstimationCode1
FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier ConvolutionsCode1
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine DomainCode1
Learning to Relate Depth and Semantics for Unsupervised Domain AdaptationCode1
Frequency-Aware Self-Supervised Monocular Depth EstimationCode1
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
HSPFormer: Hierarchical Spatial Perception Transformer for Semantic SegmentationCode1
Image Masking for Robust Self-Supervised Monocular Depth EstimationCode1
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that MatterCode1
LocalBins: Improving Depth Estimation by Learning Local DistributionsCode1
Feature-metric Loss for Self-supervised Learning of Depth and EgomotionCode1
Is Pseudo-Lidar needed for Monocular 3D Object detection?Code1
Manydepth2: Motion-Aware Self-Supervised Multi-Frame Monocular Depth Estimation in Dynamic ScenesCode1
MGNet: Monocular Geometric Scene Understanding for Autonomous DrivingCode1
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth EstimationCode1
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile DevicesCode1
Monocular Depth Estimation and Segmentation for Transparent Object with Iterative Semantic and Geometric FusionCode1
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-SupervisionCode1
RePoseD: Efficient Relative Pose Estimation With Known Depth InformationCode1
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware FusionCode1
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