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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 201–250 of 876 papers

TitleStatusHype
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular DepthCode1
LocalBins: Improving Depth Estimation by Learning Local DistributionsCode1
Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth EstimationCode1
Monocular Depth Distribution Alignment with Low ComputationCode1
Lightweight Monocular Depth Estimation through Guided DecodingCode1
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware FusionCode1
Automated Distance Estimation for Wildlife Camera TrappingCode1
Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera IntrinsicsCode1
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepthCode1
Chitransformer: Towards Reliable Stereo From CuesCode1
Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth EstimationCode1
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
Absolute distance prediction based on deep learning object detection and monocular depth estimation modelsCode1
Self-Supervised Monocular Depth Estimation with Internal Feature FusionCode1
Excavating the Potential Capacity of Self-Supervised Monocular Depth EstimationCode1
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised LearningCode1
Advancing Self-supervised Monocular Depth Learning with Sparse LiDARCode1
RVMDE: Radar Validated Monocular Depth Estimation for RoboticsCode1
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimationCode1
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth EstimationCode1
Self-supervised Monocular Depth Estimation for All Day Images using Domain SeparationCode1
Is Pseudo-Lidar needed for Monocular 3D Object detection?Code1
Towards Interpretable Deep Networks for Monocular Depth EstimationCode1
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the DarkCode1
Unsupervised Monocular Depth Estimation in Highly Complex EnvironmentsCode1
Photon-Starved Scene Inference using Single Photon CamerasCode1
CutDepth:Edge-aware Data Augmentation in Depth EstimationCode1
Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression NetworkCode1
Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated ScenesCode1
Single Image Depth Prediction with Wavelet DecompositionCode1
Unsupervised Scale-consistent Depth Learning from VideoCode1
M4Depth: Monocular depth estimation for autonomous vehicles in unseen environmentsCode1
Learning to Relate Depth and Semantics for Unsupervised Domain AdaptationCode1
Boosting Light-Weight Depth Estimation Via Knowledge DistillationCode1
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular DepthCode1
Learning optical flow from still imagesCode1
S2R-DepthNet: Learning a Generalizable Depth-specific Structural RepresentationCode1
Deep Two-View Structure-from-Motion RevisitedCode1
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-SupervisionCode1
Implicit Integration of Superpixel Segmentation into Fully Convolutional NetworksCode1
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth PredictionCode1
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection ConsistencyCode1
Monocular Depth Estimation Using Laplacian Pyramid-Based Depth ResidualsCode1
R-MSFM: Recurrent Multi-Scale Feature Modulation for Monocular Depth EstimatingCode1
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth EstimationCode1
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