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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 651–660 of 876 papers

TitleStatusHype
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation—0
Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network—0
Self-Supervised Monocular Scene Decomposition and Depth Estimation—0
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model—0
Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation—0
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic SegmentationCode0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Monocular Depth Estimation with Sharp Boundary—0
D-Net: A Generalised and Optimised Deep Network for Monocular Depth EstimationCode0
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
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