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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 126–150 of 876 papers

TitleStatusHype
Adaptive confidence thresholding for monocular depth estimationCode1
DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular VideosCode1
IEBins: Iterative Elastic Bins for Monocular Depth EstimationCode1
Implicit Integration of Superpixel Segmentation into Fully Convolutional NetworksCode1
Always Clear Depth: Robust Monocular Depth Estimation under Adverse WeatherCode1
BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth EstimationCode1
Bidirectional Attention Network for Monocular Depth EstimationCode1
Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth EstimationCode1
altiro3D: Scene representation from single image and novel view synthesisCode1
Detecting Invisible PeopleCode1
All in Tokens: Unifying Output Space of Visual Tasks via Soft TokenCode1
BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth EstimationCode1
AdaBins: Depth Estimation using Adaptive BinsCode1
Digging Into Self-Supervised Monocular Depth EstimationCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised LearningCode1
DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB ImageCode1
HSPFormer: Hierarchical Spatial Perception Transformer for Semantic SegmentationCode1
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression TasksCode1
Depth Map Decomposition for Monocular Depth EstimationCode1
Depth Map Prediction from a Single Image using a Multi-Scale Deep NetworkCode1
Deeper Depth Prediction with Fully Convolutional Residual NetworksCode1
High Quality Monocular Depth Estimation via Transfer LearningCode1
Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information FusionCode1
Harnessing Diffusion Models for Visual Perception with Meta PromptsCode1
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