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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 661–670 of 876 papers

TitleStatusHype
ROIFormer: Semantic-Aware Region of Interest Transformer for Efficient Self-Supervised Monocular Depth Estimation—0
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
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