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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 481–490 of 876 papers

TitleStatusHype
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
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