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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 351–360 of 876 papers

TitleStatusHype
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality DecoderCode0
On the Benefit of Adversarial Training for Monocular Depth EstimationCode0
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth EstimationCode0
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional ArchitectureCode0
ObjCAViT: Improving Monocular Depth Estimation Using Natural Language Models And Image-Object Cross-AttentionCode0
OmniDepth: Dense Depth Estimation for Indoors Spherical PanoramasCode0
Hierarchical Neural Memory Network for Low Latency Event ProcessingCode0
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance VotingCode0
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-trainingCode0
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