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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 771–780 of 876 papers

TitleStatusHype
PPEA-Depth: Progressive Parameter-Efficient Adaptation for Self-Supervised Monocular Depth Estimation—0
Pre-training Auto-regressive Robotic Models with 4D Representations—0
PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation—0
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction—0
Promoting CNNs with Cross-Architecture Knowledge Distillation for Efficient Monocular Depth Estimation—0
PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments—0
PS^2F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network—0
Pyramid Feature Attention Network for Monocular Depth Prediction—0
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