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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 801–825 of 876 papers

TitleStatusHype
Monocular Depth Estimation with Directional Consistency by Deep Networks—0
Learn Stereo, Infer Mono: Siamese Networks for Self-Supervised, Monocular, Depth EstimationCode0
A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation—0
Deep Optics for Monocular Depth Estimation and 3D Object Detection—0
Learning Single Camera Depth Estimation using Dual-PixelsCode0
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown CamerasCode0
Learning Across Tasks and DomainsCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth EstimationCode0
Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
Monocular 3D Object Detection with Pseudo-LiDAR Point CloudCode0
A Novel Monocular Disparity Estimation Network with Domain Transformation and Ambiguity Learning—0
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation—0
FastDepth: Fast Monocular Depth Estimation on Embedded SystemsCode0
Self-supervised Learning for Single View Depth and Surface Normal Estimation—0
Attention-based Context Aggregation Network for Monocular Depth EstimationCode0
Monocular Depth Estimation: A Survey—0
Unsupervised monocular stereo matching—0
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry PerceptionCode0
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss—0
Double Refinement Network for Efficient Indoor Monocular Depth Estimation—0
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular VideosCode0
Playing for Depth—0
Geometry meets semantics for semi-supervised monocular depth estimationCode0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
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