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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 426–450 of 876 papers

TitleStatusHype
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images—0
Deep multi-scale architectures for monocular depth estimation—0
Deep Neural Networks for Accurate Depth Estimation with Latent Space Features—0
Deep Optics for Monocular Depth Estimation and 3D Object Detection—0
Deep Planar Parallax for Monocular Depth Estimation—0
Deflating Dataset Bias Using Synthetic Data Augmentation—0
Dense Geometry Supervision for Underwater Depth Estimation—0
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
Dense monocular Simultaneous Localization and Mapping by direct surfel optimization—0
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model—0
Depth Anything in Medical Images: A Comparative Study—0
Depth Anything with Any Prior—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
DepthART: Monocular Depth Estimation as Autoregressive Refinement Task—0
Exploring Depth Contribution for Camouflaged Object Detection—0
Depth Estimation and Image Restoration by Deep Learning from Defocused Images—0
Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus—0
Depth Estimation from Single Image using Sparse Representations—0
Depth Estimation with Simplified Transformer—0
DepthFake: a depth-based strategy for detecting Deepfake videos—0
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions—0
Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation—0
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