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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 426–450 of 876 papers

TitleStatusHype
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth EstimationCode0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR—0
D-NPC: Dynamic Neural Point Clouds for Non-Rigid View Synthesis from Monocular Video—0
Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion—0
ToSA: Token Selective Attention for Efficient Vision Transformers—0
Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid TransformerCode0
Back to the Color: Learning Depth to Specific Color Transformation for Unsupervised Depth EstimationCode0
MambaDepth: Enhancing Long-range Dependency for Self-Supervised Fine-Structured Monocular Depth Estimation—0
Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry—0
Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian—0
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation—0
Estimating Depth of Monocular Panoramic Image with Teacher-Student Model Fusing Equirectangular and Spherical Representations—0
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth EstimationCode0
Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning—0
Depth Prompting for Sensor-Agnostic Depth EstimationCode0
FA-Depth: Toward Fast and Accurate Self-supervised Monocular Depth EstimationCode0
A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose—0
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation—0
Depth Priors in Removal Neural Radiance Fields—0
Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting—0
Promoting CNNs with Cross-Architecture Knowledge Distillation for Efficient Monocular Depth Estimation—0
MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images—0
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