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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
CLIP Can Understand Depth—0
Diffusion-based Light Field Synthesis—0
Depth Anything in Medical Images: A Comparative Study—0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency—0
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes—0
NeRFmentation: NeRF-based Augmentation for Monocular Depth Estimation—0
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers—0
PPEA-Depth: Progressive Parameter-Efficient Adaptation for Self-Supervised Monocular Depth Estimation—0
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model—0
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