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A large-scale hierarchical multi-view RGB-D object dataset
TLDR
A large-scale, hierarchical multi-view object dataset collected using anRGB-D camera is introduced and techniques for RGB-D based object recognition and detection are introduced, demonstrating that combining color and depth information substantially improves quality of results.
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Learning a classification model for segmentation
TLDR
A two-class classification model for grouping is proposed that defines a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation, and trains a linear classifier to combine these features.
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Learning to recognize objects in egocentric activities
TLDR
The key to this approach is a robust, unsupervised bottom up segmentation method, which exploits the structure of the egocentric domain to partition each frame into hand, object, and background categories and uses Multiple Instance Learning to match object instances across sequences.
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RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments
TLDR
This paper presents RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment to achieve globally consistent maps.
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RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
TLDR
This paper presents RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment to achieve globally consistent maps.
View on SAGE
Unsupervised Feature Learning for RGB-D Based Object Recognition
TLDR
HMP uses sparse coding to learn hierarchical feature representations from raw RGB-D data in an unsupervised way and enables superior object recognition results using linear support vector machines.
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Kernel Descriptors for Visual Recognition
TLDR
This work highlights the kernel view of orientation histograms, and shows that they are equivalent to a certain type of match kernels over image patches, and designs a family of kernel descriptors which provide a unified and principled framework to turn pixel attributes into compact patch-level features.
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RGB-(D) scene labeling: Features and algorithms
TLDR
The main objective is to empirically understand the promises and challenges of scene labeling with RGB-D and adapt the framework of kernel descriptors that converts local similarities (kernels) to patch descriptors to capture appearance (RGB) and shape (D) similarities.
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Depth kernel descriptors for object recognition
TLDR
A set of kernel features on depth images that model size, 3D shape, and depth edges in a single framework that significantly improve the capabilities of depth and RGB-D (color+depth) recognition, achieving 10–15% improvement in accuracy over the state of the art.
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Discriminatively Trained Sparse Code Gradients for Contour Detection
TLDR
This work shows that contour detection accuracy can be significantly improved by computing Sparse Code Gradients (SCG), which measure contrast using patch representations automatically learned through sparse coding, which is verified on the NYU Depth Dataset.
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