RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments

By Peter Henry, Michael Krainin, Evan V. Herbst, Xiaofeng Ren, Dieter Fox. 2012

1,253 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Geology

Citations in the corpus

Learning human activities and object affordances from RGB-D videos (2012, background): “Recent works in this area have addressed tasks such as estimating geometry (Henry et al., 2012), tracking objects (Choi and Christensen, 2012), recognizing objects (Collet et al., 2011), placing objects (Jiang et al., 2012b) and labeling geometric classes (Koppula et al., 2011; Anand et al., 2012).”
(CAD)$^2$RL: Real Single-Image Flight without a Single Real Image (2016, methodology): “The 3D geometry of the local environment can be deduced using SLAM with range sensors [3], consumer depth sensors [44, 16], stereo camera pairs [37], as well as monocular cameras [8].”
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects (2018, background): “More recently, surfel-based map representations were also introduced [18, 23] to the domain of RGBD-SLAM.”
3-D Mapping With an RGB-D Camera (2014, methodology): “The first scientifically published RGB-D SLAM system was proposed by Henry et al. [17], who used visual features in combination with Generalized-ICP [18] to create and optimize a pose graph.”
Nonparametric Calibration for Depth Sensors (2015, background): “Depth sensors likeMicrosoft Kinect or ASUSXtion aremajor technological achievements and gave a new impulse to a wide range of 3D applications such as SLAM [3, 5, 10], superresolution mapping [7], object recognition and many others.”
School of Electrical and Computer Engineering Oklahoma State University , Stillwater , OK 74078 , USA (methodology): “In general, researchers in the robotics community deploy Simultaneous Localization and Mapping (SLAM) framework for sparse 3D mapping using visual and depth information, and their ambition is ultimately to minimize the trajectory error [14, 23, 24, 17, 26] with less concern about the pertinent detai”
Unsupervised Intrinsic Calibration of Depth Sensors via SLAM (2013, methodology): “Our SLAM approach is similar to that of RGBD- SLAM [6] [5] or RGB-D Mapping [9] [8], but with some modifications.”
VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction (2016, methodology): “Online Static Reconstruction : Methods for ofﬂine static 3D shape reconstruction from partial RGB-D scans differ in the employed scene representation, such as point-based representations [19–21] or meshes [22].”
TEASER: Fast and Certifiable Point Cloud Registration (2020, background): “It finds applications in motion estimation and 3-D reconstruction [3]–[6], object recognition and localization [7]–[10], panorama stitching [11], and medical imaging [12], [13], to name a few.”
Robust real-time visual odometry for dense RGB-D mapping (2013, background): “The advent of the Microsoft Kinect and other RGB-D sensors has resulted in great progress in dense mapping and SLAM in recent years [2], [3], [4].”
KinectFaceDB: A Kinect Database for Face Recognition (2014, background): “Thanks to its efficiency, low-cost, ease of RGB-D mapping, and multimodal sensing, the Kinect sensor has received vast amount of attention from diverse research communities [2], including but not limited in computer vision [3], computer graphics [4], [5], augmented reality (AR) [4], human–computer-i”
A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation (2018, methodology): “Between these two extremes, camera-based solutions, especially those developed around Microsoft Kinect including [253]–[256], provide a good balance between cost (in terms of equipment price and survey time) and map accuracy.”
An interactive approach to semantic modeling of indoor scenes with an RGBD camera (2012, methodology): “Similar to the registration algorithm in [Henry et al. 2012; Du et al. 2011], we also perform the RANSAC algo-rithm to compute the correct correspondences between frames us-ing the SIFT feature [Lowe 2004], and the semantic labels are used to cull the wrong correspondences.”
Real-Time 3D Reconstruction in Dynamic Scenes Using Point-Based Fusion (2013, methodology): “Such methods have therefore been used in larger sized reconstructions [4, 20].”
An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics (2015, methodology): “SLAM approaches have been categorized into filtering approaches (such as EKF-SLAM [106] and particle filter based SLAM [81]) and smoothing approaches (such as GraphSLAM [111], RGBD SLAM [56], Smoothing and Mapping [30]).”
Visual SLAM algorithms: a survey from 2010 to 2016 (2017, background): “In the literature [62, 63], relative camera motion is estimated by tracking feature points between successive frames.”