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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 401–425 of 876 papers

TitleStatusHype
S^2Net: Accurate Panorama Depth Estimation on Spherical Surface—0
A Study on the Generality of Neural Network Structures for Monocular Depth EstimationCode1
Deep Planar Parallax for Monocular Depth Estimation—0
DepthP+P: Metric Accurate Monocular Depth Estimation using Planar and Parallax—0
All in Tokens: Unifying Output Space of Visual Tasks via Soft TokenCode1
BS3D: Building-scale 3D Reconstruction from RGB-D Images—0
3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces—0
LightedDepth: Video Depth Estimation in Light of Limited Inference View AnglesCode1
Trap Attention: Monocular Depth Estimation With Manual TrapsCode1
Exploring Efficiency of Vision Transformers for Self-Supervised Monocular Depth EstimationCode0
MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth EstimationCode1
Lightweight Monocular Depth Estimation—0
ROIFormer: Semantic-Aware Region of Interest Transformer for Efficient Self-Supervised Monocular Depth Estimation—0
Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth EstimationCode1
Event-based Monocular Dense Depth Estimation with Recurrent Transformers—0
3D Object Aided Self-Supervised Monocular Depth Estimation—0
Multi-resolution Monocular Depth Map Fusion by Self-supervised Gradient-based CompositionCode1
ObjCAViT: Improving Monocular Depth Estimation Using Natural Language Models And Image-Object Cross-AttentionCode0
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object DetectionCode0
Self-Supervised Surround-View Depth Estimation with Volumetric Feature FusionCode1
3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection TransformersCode1
Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth EstimationCode2
The Monocular Depth Estimation ChallengeCode1
Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimation—0
A Practical Stereo Depth System for Smart GlassesCode1
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