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Depth from Familiar Objects: A Hierarchical Model for 3D Scenes

https://doi.org/10.1109/cvpr.2006.97

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Image Parsing: Unifying Segmentation, Detection, and Recognition

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Robust wide-baseline stereo from maximally stable extremal regions

inferredcites →https://doi.org/10.1023/b:visi.0000027790.02288.f2openalex-snapshot

Scale & Affine Invariant Interest Point Detectors

inferredcites →https://doi.org/10.1023/b:visi.0000029664.99615.94openalex-snapshot

Distinctive Image Features from Scale-Invariant Keypoints

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Bayesian Density Estimation and Inference Using Mixtures

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Markov Chain Sampling Methods for Dirichlet Process Mixture Models

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Learning flexible sprites in video layers

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Object class recognition by unsupervised scale-invariant learning

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A maximum entropy framework for part-based texture and object recognition

inferredcites →https://doi.org/10.1109/iccv.2005.107openalex-snapshot

Geometric context from a single image

inferredcites →https://doi.org/10.1109/iccv.2005.137openalex-snapshot

Learning hierarchical models of scenes, objects, and parts

inferredcites →https://doi.org/10.1109/iccv.2005.226openalex-snapshot

Simultaneous multiple 3D motion estimation via mode finding on Lie groups

inferredcites →https://doi.org/10.1109/iccv.2005.77openalex-snapshot

Discovering objects and their location in images

inferredcites →https://doi.org/10.1109/tpami.2002.1033214openalex-snapshot

Depth estimation from image structure

inferredcites →https://doi.org/10.1198/016214506000000302openalex-snapshot

Hierarchical Dirichlet Processes

inferredcites →https://doi.org/10.1201/9780429258411openalex-snapshot

Bayesian Data Analysis

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia

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Closing the loop in scene interpretation

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Recognition using regions

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Toward coherent object detection and scene layout understanding

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Semantic structure from motion

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Learning the right model: Efficient max-margin learning in Laplacian CRFs

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Learning Structured Hough Voting for Joint Object Detection and Occlusion Reasoning

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Efficient Scene Layout Aware Object Detection for Traffic Surveillance

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Depth estimation in monocular Breast Self-Examination image sequence using optical flow

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Choosing informative actions for manipulation tasks

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Depth Estimation from a Single Image using Multi Stream and Scale Deep Learning

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Two-View Motion Segmentation by Mixtures of Dirichlet Process with Model Selection and Outlier Removal

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Depth Information by Stage Classification

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Color constancy using 3D scene geometry

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Recovering the spatial layout of cluttered rooms

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Context by region ancestry

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DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding

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Semantic structure from motion with object and point interactions

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Exploiting a scene calibration mechanism for depth estimation

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Color Constancy Using 3D Scene Geometry Derived From a Single Image

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Learning a Layout Transfer Network for Context Aware Object Detection

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Make3D: Learning 3D Scene Structure from a Single Still Image

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Stages as Models of Scene Geometry

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Smaller Residual Network for Single Image Depth Estimation

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