Monocular Depth Estimation Using Multi-Scale Continuous CRFs as Sequential Deep Networks
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- Type
- article
- Published
- 2018-03-01
- Cited by
- 112
- References
- 53
- Access
- Open access
- OpenAlex
- https://openalex.org/W29994300
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3681302
Keywords
Mathematics
References
- Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Learning Deconvolution Network for Semantic Segmentation
- Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
- Fully convolutional networks for semantic segmentation
- Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture
- Towards unified depth and semantic prediction from a single image
- DeepEdge: A multi-scale bifurcated deep network for top-down contour detection
- Indoor scene structure analysis for single image depth estimation
- Hypercolumns for object segmentation and fine-grained localization
- Fast High‐Dimensional Filtering Using the Permutohedral Lattice
- Pulling Things out of Perspective
- Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling
- Discrete-Continuous Depth Estimation from a Single Image
- Fully Connected Deep Structured Networks
- A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image
- Vision meets robotics: The KITTI dataset
- Conditional Random Fields as Recurrent Neural Networks
- Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs
- Deep convolutional neural fields for depth estimation from a single image
Cited by
- Deep learning and conditional random fields‐based depth estimation and topographical reconstruction from conventional endoscopy
- Measuring the absolute distance of a front vehicle from an in-car camera based on monocular vision and instance segmentation
- Unsupervised Adversarial Depth Estimation Using Cycled Generative Networks
- Rethinking Monocular Depth Estimation with Adversarial Training
- More interesting regions: an efficient road segmentation method based on vanishing point
- Super-Resolution for Monocular Depth Estimation With Multi-Scale Sub-Pixel Convolutions and a Smoothness Constraint
- Monocular Depth Estimation: A Survey
- Contributions to deep learning methodologies
- Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation
- Learning the Depths of Moving People by Watching Frozen People
- Step-by-step Erasion, One-by-one Collection: A Weakly Supervised Temporal Action Detector
- A Robust Billboard-based Free-viewpoint Video Synthesizing Algorithm for Sports Scenes
- Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation
- Monocular depth estimation with geometrical guidance using a multi-level convolutional neural network
- Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection
- Progressive Fusion for Unsupervised Binocular Depth Estimation Using Cycled Networks
- Discern Depth Under Foul Weather: Estimate PM_2.5 for Depth Inference
- Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM
- From Coarse to Fine: A Monocular Depth Estimation Model Based on Left-Right Consistency
- Improving Monocular Depth Prediction in Ambiguous Scenes Using a Single Range Measurement
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