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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 421–430 of 876 papers

TitleStatusHype
UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation—0
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation—0
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches—0
ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth EstimationCode0
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
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
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