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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 351–375 of 876 papers

TitleStatusHype
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled NetworksCode0
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motionCode0
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional ArchitectureCode0
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic SegmentationCode0
HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality DecoderCode0
PhaseCam3D — Learning Phase Masks for Passive Single View Depth EstimationCode0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
On the Benefit of Adversarial Training for Monocular Depth EstimationCode0
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth EstimationCode0
Hierarchical Neural Memory Network for Low Latency Event ProcessingCode0
On Robust Cross-View Consistency in Self-Supervised Monocular Depth EstimationCode0
OmniDepth: Dense Depth Estimation for Indoors Spherical PanoramasCode0
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-trainingCode0
ObjCAViT: Improving Monocular Depth Estimation Using Natural Language Models And Image-Object Cross-AttentionCode0
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth EstimationCode0
Multi-task Learning for Monocular Depth and Defocus Estimations with Real ImagesCode0
Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted DataCode0
Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth EstimationCode0
Geometry meets semantics for semi-supervised monocular depth estimationCode0
Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid TransformerCode0
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth EstimationCode0
Generating and Exploiting Probabilistic Monocular Depth EstimatesCode0
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance VotingCode0
Monocular Depth Estimation Using Cues Inspired by Biological Vision SystemsCode0
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