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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 781–790 of 876 papers

TitleStatusHype
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional ArchitectureCode0
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motionCode0
Fast Scene Understanding for Autonomous DrivingCode0
Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional NetworksCode0
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature ReconstructionCode0
FastDepth: Fast Monocular Depth Estimation on Embedded SystemsCode0
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic SegmentationCode0
False Negative Reduction in Semantic Segmentation under Domain Shift using Depth EstimationCode0
FA-Depth: Toward Fast and Accurate Self-supervised Monocular Depth EstimationCode0
Revisiting Self-Supervised Monocular Depth EstimationCode0
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