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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 351–375 of 876 papers

TitleStatusHype
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention—0
Camera-Only Bird's Eye View Perception: A Neural Approach to LiDAR-Free Environmental Mapping for Autonomous Vehicles—0
Diffusion-based Light Field Synthesis—0
Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy—0
Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation—0
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency—0
Depth-Relative Self Attention for Monocular Depth Estimation—0
Calibrating Self-supervised Monocular Depth Estimation—0
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation—0
ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way—0
BulletGen: Improving 4D Reconstruction with Bullet-Time Generation—0
Depth Priors in Removal Neural Radiance Fields—0
ViewpointDepth: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts—0
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation—0
BS3D: Building-scale 3D Reconstruction from RGB-D Images—0
DepthP+P: Metric Accurate Monocular Depth Estimation using Planar and Parallax—0
Bridging Geometric and Semantic Foundation Models for Generalized Monocular Depth Estimation—0
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation—0
An Endoscopic Chisel: Intraoperative Imaging Carves 3D Anatomical Models—0
Learning Good Features to Transfer Across Tasks and Domains—0
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation—0
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World—0
Depth Is All You Need for Monocular 3D Detection—0
Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation—0
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions—0
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