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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 801–850 of 876 papers

TitleStatusHype
Real-time Monocular Depth Estimation on Embedded Systems—0
S^2Net: Accurate Panorama Depth Estimation on Spherical Surface—0
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation—0
SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates—0
Scalable Autoregressive Monocular Depth Estimation—0
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation—0
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy—0
Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency—0
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues—0
Self-supervised Learning for Single View Depth and Surface Normal Estimation—0
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera—0
Self-supervised Monocular Depth Estimation with Large Kernel Attention—0
Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining—0
Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation—0
Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior—0
Self-Supervised Monocular Scene Decomposition and Depth Estimation—0
Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning—0
SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation—0
Semi-Supervised Adversarial Monocular Depth Estimation—0
Semi-Supervised Deep Learning for Monocular Depth Map Prediction—0
Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation—0
SG-MIM: Structured Knowledge Guided Efficient Pre-training for Dense Prediction—0
SGTBN: Generating Dense Depth Maps from Single-Line LiDAR—0
SLAM Endoscopy enhanced by adversarial depth prediction—0
SoftEnNet: Symbiotic Monocular Depth Estimation and Lumen Segmentation for Colonoscopy Endorobots—0
Soft Labels for Ordinal Regression—0
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images—0
Spatially Visual Perception for End-to-End Robotic Learning—0
Spatial RoboGrasp: Generalized Robotic Grasping Control Policy—0
SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events—0
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications—0
STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular Depth Estimation—0
StereoGen: High-quality Stereo Image Generation from a Single Image—0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
Structure-Attentioned Memory Network for Monocular Depth Estimation—0
Structure-Aware Radar-Camera Depth Estimation—0
Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation—0
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation—0
Structured Depth Prediction in Challenging Monocular Video Sequences—0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Survey on Monocular Metric Depth Estimation—0
SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection—0
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving—0
TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs—0
Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings—0
The Second Monocular Depth Estimation Challenge—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
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