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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
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
High Quality Monocular Depth Estimation via Transfer LearningCode1
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
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
Playing for Depth—0
Geometry meets semantics for semi-supervised monocular depth estimationCode0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
MERCI: A NEW METRIC TO EVALUATE THE CORRELATION BETWEEN PREDICTIVE UNCERTAINTY AND TRUE ERROR—0
Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy MapsCode0
Style Augmentation: Data Augmentation via Style RandomizationCode0
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric AnnotationsCode0
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation—0
Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss—0
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery—0
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