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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 701–710 of 876 papers

TitleStatusHype
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
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