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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 526–550 of 876 papers

TitleStatusHype
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks—0
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion—0
Automated Distance Estimation for Wildlife Camera TrappingCode1
Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera IntrinsicsCode1
Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings—0
Maximum Likelihood Uncertainty Estimation: Robustness to OutliersCode0
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth—0
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation—0
GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding—0
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepthCode1
A Survey on RGB-D DatasetsCode2
Multi-Robot Collaborative Perception with Graph Neural Networks—0
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation—0
360MonoDepth: High-Resolution 360deg Monocular Depth EstimationCode2
Generalizing Interactive Backpropagating Refinement for Dense Prediction Networks—0
Neural Window Fully-Connected CRFs for Monocular Depth Estimation—0
Chitransformer: Towards Reliable Stereo From CuesCode1
Improving Depth Estimation using Location Information—0
Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth EstimationCode1
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis—0
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional NetworkCode1
Toward Practical Monocular Indoor Depth EstimationCode1
A benchmark with decomposed distribution shifts for 360 monocular depth estimationCode1
360MonoDepth: High-Resolution 360° Monocular Depth EstimationCode1
SUB-Depth: Self-distillation and Uncertainty Boosting Self-supervised Monocular Depth EstimationCode1
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