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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 376–400 of 876 papers

TitleStatusHype
Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting—0
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World—0
Analysis of NaN Divergence in Training Monocular Depth Estimation Model—0
Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation—0
Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method—0
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions—0
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes—0
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth—0
EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer—0
Lightweight Monocular Depth Estimation with an Edge Guided Network—0
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation—0
An Advert Creation System for 3D Product Placements—0
Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces—0
Depth Is All You Need for Monocular 3D Detection—0
Recovering Detail in 3D Shapes Using Disparity Maps—0
Increased-Range Unsupervised Monocular Depth Estimation—0
Improving the Reliability for Confidence Estimation—0
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth—0
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos—0
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion—0
Improving Online Performance Prediction for Semantic Segmentation—0
Improving Monocular Visual Odometry Using Learned Depth—0
DepthFake: a depth-based strategy for detecting Deepfake videos—0
Learn to Adapt for Monocular Depth Estimation—0
Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding—0
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