KinectFusion: Real-time dense surface mapping and tracking
Richard Newcombe,Shahram Izadi,Otmar Hilliges,David Molyneaux,David Kim,Andrew J. Davison,Pushmeet Kohi,Jamie Shotton,Steve Hodges,Andrew Fitzgibbon +9 more
- pp 127-136
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TLDR
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.Abstract:
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. We 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. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse-to-fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame-to-frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real-time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR), in particular, it allows dense surfaces to be reconstructed in real-time, with a level of detail and robustness beyond any solution yet presented using passive computer vision.read more
Citations
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Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Journal ArticleDOI
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Raul Mur-Artal,Juan D. Tardós +1 more
TL;DR: ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities, is presented, being in most cases the most accurate SLAM solution.
Proceedings ArticleDOI
A benchmark for the evaluation of RGB-D SLAM systems
TL;DR: A large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system is recorded for the evaluation of RGB-D SLAM systems.
Journal ArticleDOI
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
Raul Mur-Artal,Juan D. Tardós +1 more
TL;DR: ORB-SLAM2 as mentioned in this paper is a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities.
Proceedings ArticleDOI
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
Shahram Izadi,David Kim,Otmar Hilliges,David Molyneaux,Richard Newcombe,Pushmeet Kohli,Jamie Shotton,Steve Hodges,Dustin Freeman,Andrew J. Davison,Andrew Fitzgibbon +10 more
TL;DR: 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.
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