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Indoor Segmentation and Support Inference from RGBD Images
We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only toExpand
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  • 526
  • Open Access
KinectFusion: Real-time dense surface mapping and tracking
We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware.Expand
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KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to track the 3D poseExpand
  • 1,820
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Robust Higher Order Potentials for Enforcing Label Consistency
This paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. Our method is based on higher order conditional random fields andExpand
  • 954
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Relational inductive biases, deep learning, and graph networks
Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheapExpand
  • 722
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Associative hierarchical CRFs for object class image segmentation
Most methods for object class segmentation are formulated as a labelling problem over a single choice of quantisation of an image space - pixels, segments or group of segments. It is well known thatExpand
  • 645
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Instructing people for training gestural interactive systems
Entertainment and gaming systems such as the Wii and XBox Kinect have brought touchless, body-movement based interfaces to the masses. Systems like these enable the estimation of movements of variousExpand
  • 347
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Graph Cut Based Inference with Co-occurrence Statistics
Markov and Conditional random fields (CRFs) used in computer vision typically model only local interactions between variables, as this is computationally tractable. In this paper we consider a classExpand
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Deep Convolutional Inverse Graphics Network
This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that aims to learn an interpretable representation of images, disentangled with respect to three-dimensional sceneExpand
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Efficient regression of general-activity human poses from depth images
We present a new approach to general-activity human pose estimation from depth images, building on Hough forests. We extend existing techniques in several ways: real time prediction of multiple 3DExpand
  • 384
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  • Open Access