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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 476–500 of 876 papers

TitleStatusHype
PS^2F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing—0
Can Language Understand Depth?Code1
Recovering Detail in 3D Shapes Using Disparity Maps—0
MGNet: Monocular Geometric Scene Understanding for Autonomous DrivingCode1
LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic SegmentationCode1
Monocular Depth Decomposition of Semi-Transparent Volume RenderingsCode0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation TechniquesCode0
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks—0
Revealing the Dark Secrets of Masked Image ModelingCode1
Single-View View Synthesis in the Wild with Learned Adaptive Multiplane ImagesCode2
Deep Digging into the Generalization of Self-Supervised Monocular Depth EstimationCode1
Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous DrivingCode1
Learning Monocular Depth Estimation via Selective Distillation of Stereo Knowledge—0
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras—0
Outdoor Monocular Depth Estimation: A Research Review—0
Overcoming the Distance Estimation Bottleneck in Estimating Animal Abundance with Camera TrapsCode1
Depth Estimation with Simplified Transformer—0
Monocular Depth Estimation Using Cues Inspired by Biological Vision SystemsCode0
Multi-Frame Self-Supervised Depth with Transformers—0
End-to-end Learning for Joint Depth and Image Reconstruction from Diffracted Rotation—0
HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model—0
SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth EstimationCode2
Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth Estimation—0
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