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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 211–220 of 876 papers

TitleStatusHype
Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth EstimationCode1
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional NetworkCode1
Toward Practical Monocular Indoor Depth EstimationCode1
A benchmark with decomposed distribution shifts for 360 monocular depth estimationCode1
360MonoDepth: High-Resolution 360° Monocular Depth EstimationCode1
SUB-Depth: Self-distillation and Uncertainty Boosting Self-supervised Monocular Depth EstimationCode1
Absolute distance prediction based on deep learning object detection and monocular depth estimation modelsCode1
Self-Supervised Monocular Depth Estimation with Internal Feature FusionCode1
Excavating the Potential Capacity of Self-Supervised Monocular Depth EstimationCode1
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised LearningCode1
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