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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 451–460 of 876 papers

TitleStatusHype
Self-distilled Feature Aggregation for Self-supervised Monocular Depth EstimationCode1
BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth EstimationCode1
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile DevicesCode1
Rethinking Skip Connections in Encoder-decoder Networks for Monocular Depth Estimation—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
Depth Map Decomposition for Monocular Depth EstimationCode1
DepthFake: a depth-based strategy for detecting Deepfake videos—0
Multi-task Learning for Monocular Depth and Defocus Estimations with Real ImagesCode0
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision TransformerCode1
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