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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 826–850 of 876 papers

TitleStatusHype
SLAM Endoscopy enhanced by adversarial depth prediction—0
SoftEnNet: Symbiotic Monocular Depth Estimation and Lumen Segmentation for Colonoscopy Endorobots—0
Soft Labels for Ordinal Regression—0
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images—0
Spatially Visual Perception for End-to-End Robotic Learning—0
Spatial RoboGrasp: Generalized Robotic Grasping Control Policy—0
SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events—0
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications—0
STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular Depth Estimation—0
StereoGen: High-quality Stereo Image Generation from a Single Image—0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
Structure-Attentioned Memory Network for Monocular Depth Estimation—0
Structure-Aware Radar-Camera Depth Estimation—0
Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation—0
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation—0
Structured Depth Prediction in Challenging Monocular Video Sequences—0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Survey on Monocular Metric Depth Estimation—0
SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection—0
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving—0
TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs—0
Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings—0
The Second Monocular Depth Estimation Challenge—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
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