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Proceedings ArticleDOI

KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera

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TLDR
Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction, to enable real-time multi-touch interactions anywhere.
Abstract
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 pose of the sensor and reconstruct, geometrically precise, 3D models of the physical scene in real-time. The capabilities of KinectFusion, as well as the novel GPU-based pipeline are described in full. Uses of the core system for low-cost handheld scanning, and geometry-aware augmented reality and physics-based interactions are shown. Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction. These extensions are used to enable real-time multi-touch interactions anywhere, allowing any planar or non-planar reconstructed physical surface to be appropriated for touch.

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Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers.

TL;DR: In this article, a new computational framework for real-time 3D scene reconstruction from single-photon data is proposed, which can handle an unknown number of surfaces in each pixel, allowing for target detection and imaging through cluttered scenes.
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Edge guided single depth image super resolution

TL;DR: A novel framework for single depth image super resolution guided by a high resolution edge map constructed from the edges in the low resolution depth image via a Markov Random Field (MRF) optimization is proposed.
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Sparse depth super resolution

TL;DR: A method to produce detailed high resolution depth maps from aggressively subsampled depth measurements using an image combined with a low resolution depth map that outperforms recent state of the art methods quantitatively and qualitatively.
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A Deep Primal-Dual Network for Guided Depth Super-Resolution

TL;DR: A novel method to increase the spatial resolution of depth images by combining a deep fully convolutional network with a non-local variational method in a deep primal-dual network that outperforms the state-of-the-art on multiple benchmarks.
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Air-ground localization and map augmentation using monocular dense reconstruction

TL;DR: A novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement is presented, which achieves high accuracy whereas appearance-based, state-of-the-art approaches fail.
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Proceedings ArticleDOI

KinectFusion: Real-time dense surface mapping and tracking

TL;DR: 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, which fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real- time.
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