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Unsupervised Monocular Depth Estimation

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Showing 1–50 of 59 papers

TitleStatusHype
Toward Better SSIM Loss for Unsupervised Monocular Depth Estimation—0
Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy—0
ProDepth: Boosting Self-Supervised Multi-Frame Monocular Depth with Probabilistic FusionCode1
SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint LearningCode1
Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion—0
Back to the Color: Learning Depth to Specific Color Transformation for Unsupervised Depth EstimationCode0
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth EstimationCode2
Digging into contrastive learning for robust depth estimation with diffusion modelsCode1
Deeper into Self-Supervised Monocular Indoor Depth EstimationCode1
MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable UncertaintyCode1
Continual Learning of Unsupervised Monocular Depth from VideosCode0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenesCode1
WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather ConditionsCode1
InfraParis: A multi-modal and multi-task autonomous driving datasetCode0
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost VolumeCode1
Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution NetworkCode1
Self-supervised Monocular Depth Estimation: Let's Talk About The WeatherCode1
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motionCode0
Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes—0
RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic ScenesCode1
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation—0
Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth EstimationCode2
SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic ScenesCode2
Towards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation—0
Frequency-Aware Self-Supervised Monocular Depth EstimationCode1
Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening ProblemCode1
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision TransformerCode1
TransDSSL: Transformer based Depth Estimation via Self-Supervised LearningCode1
Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion DynamicsCode2
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular DepthCode1
Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network—0
Self-Supervised Monocular Depth Estimation with Internal Feature FusionCode1
Unsupervised Monocular Depth Estimation in Highly Complex EnvironmentsCode1
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular DepthCode1
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection ConsistencyCode1
HR-Depth: High Resolution Self-Supervised Monocular Depth EstimationCode1
Unsupervised Monocular Depth Learning in Dynamic ScenesCode1
Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature AdaptationCode1
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation—0
Increased-Range Unsupervised Monocular Depth Estimation—0
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations—0
The Edge of Depth: Explicit Constraints between Segmentation and DepthCode1
Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation—0
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera—0
Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation—0
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown CamerasCode0
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