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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 551–575 of 876 papers

TitleStatusHype
Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One—0
Error Diagnosis of Deep Monocular Depth Estimation Models—0
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation—0
Absolute distance prediction based on deep learning object detection and monocular depth estimation modelsCode1
CamLessMonoDepth: Monocular Depth Estimation with Unknown Camera Parameters—0
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation—0
Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network—0
Self-Supervised Monocular Scene Decomposition and Depth Estimation—0
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model—0
Self-Supervised Monocular Depth Estimation with Internal Feature FusionCode1
Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic SegmentationCode0
Monocular Depth Estimation with Sharp Boundary—0
D-Net: A Generalised and Optimised Deep Network for Monocular Depth EstimationCode0
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
Excavating the Potential Capacity of Self-Supervised Monocular Depth EstimationCode1
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data—0
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised LearningCode1
Advancing Self-supervised Monocular Depth Learning with Sparse LiDARCode1
RVMDE: Radar Validated Monocular Depth Estimation for RoboticsCode1
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
Multi-task learning from fixed-wing UAV images for 2D/3D city modeling—0
Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss—0
Lightweight Monocular Depth with a Novel Neural Architecture Search Method—0
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