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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 476–500 of 876 papers

TitleStatusHype
Aperture Supervision for Monocular Depth Estimation—0
A Simple Baseline for Supervised Surround-view Depth Estimation—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images—0
A Weakly-Supervised Depth Estimation Network Using Attention Mechanism—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP—0
Balancing Shared and Task-Specific Representations: A Hybrid Approach to Depth-Aware Video Panoptic Segmentation—0
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation—0
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
BlindSpotNet: Seeing Where We Cannot See—0
Bokeh Rendering Based on Adaptive Depth Calibration Network—0
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces—0
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion—0
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth—0
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth—0
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World—0
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation—0
Bridging Geometric and Semantic Foundation Models for Generalized Monocular Depth Estimation—0
BS3D: Building-scale 3D Reconstruction from RGB-D Images—0
BulletGen: Improving 4D Reconstruction with Bullet-Time Generation—0
ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way—0
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