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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 811–820 of 876 papers

TitleStatusHype
ObjCAViT: Improving Monocular Depth Estimation Using Natural Language Models And Image-Object Cross-AttentionCode0
ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth EstimationCode0
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-trainingCode0
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance VotingCode0
Multi-task Learning for Monocular Depth and Defocus Estimations with Real ImagesCode0
Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted DataCode0
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth EstimationCode0
Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid TransformerCode0
Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth EstimationCode0
Back to the Color: Learning Depth to Specific Color Transformation for Unsupervised Depth EstimationCode0
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