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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 791–800 of 876 papers

TitleStatusHype
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object BoundariesCode0
PhaseCam3D — Learning Phase Masks for Passive Single View Depth EstimationCode0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth EstimationCode0
WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth EstimationCode0
On the Benefit of Adversarial Training for Monocular Depth EstimationCode0
Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation TechniquesCode0
Exploring Efficiency of Vision Transformers for Self-Supervised Monocular Depth EstimationCode0
Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map PredictionCode0
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion SegmentationCode0
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