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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 301–325 of 876 papers

TitleStatusHype
Uni-DVPS: Unified Model for Depth-Aware Video Panoptic SegmentationCode1
Deeper into Self-Supervised Monocular Indoor Depth EstimationCode1
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection ConsistencyCode1
Learning Stereo from Single ImagesCode1
Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth EstimationCode1
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic ImageryCode0
Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth EstimationCode0
RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language DescriptionsCode0
Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map PredictionCode0
Edge-Guided Occlusion Fading Reduction for a Light-Weighted Self-Supervised Monocular Depth EstimationCode0
Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch AttackCode0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
SaccadeCam: Adaptive Visual Attention for Monocular Depth SensingCode0
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion SegmentationCode0
Revisiting Self-Supervised Monocular Depth EstimationCode0
Dual CNN Models for Unsupervised Monocular Depth EstimationCode0
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object BoundariesCode0
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature ExtractionCode0
Recurrent Scene Parsing with Perspective Understanding in the LoopCode0
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric AnnotationsCode0
Adversarial Attacks on Monocular Pose EstimationCode0
ClearGrasp: 3D Shape Estimation of Transparent Objects for ManipulationCode0
On the Viability of Monocular Depth Pre-training for Semantic SegmentationCode0
D-Net: A Generalised and Optimised Deep Network for Monocular Depth EstimationCode0
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional ArchitectureCode0
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