RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments

By Peter Henry, Michael Krainin, Evan V. Herbst, Xiaofeng Ren, Dieter Fox. International Symposium on Experimental Robotics, 2010

1,039 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/849eaeeef1e11280bb7812239d34712b30023165

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Citations in the corpus

Learning hierarchical sparse features for RGB-(D) object recognition (2014, methodology): “For instance, the Microsoft Kinect sensor, developed by Prime-Sense, provides 640 × 480 color and depth images at 30 frames per second and has been successfully used for computer gaming (Shotton et al., 2011) and 3D reconstruction (Henry et al., 2010; Newcombe et al., 2011).”
A large-scale hierarchical multi-view RGB-D object dataset (2011, methodology): “[15] show that this allows the system to handle situations in which only vision or shape alone would fail to generate good alignments.”
Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications (2012, neutral): “[16].”
Manipulator and object tracking for in-hand 3D object modeling (2011, methodology): “’s RGB-D mapping (Henry et al., 2010) and Strobl et al.”
Real-time visual odometry from dense RGB-D images (2011, background): “In contrast, many state-of-the-art approaches using monocular camera images extract key-points and match them with previous frames [3] [9] [2] using a sequence of processing steps including descriptor matching, RANSAC and bundle adjustment.”
Cognitive Mapping and Planning for Visual Navigation (2017, background): “…studied in computer vision and robotics in the form of structure from motion and simultaneous localization and mapping (Fuentes-Pacheco et al. 2015; Izadi et al. 2011; Henry et al. 2010; Snavely et al. 2008) with a variety of sensing modalities such as range sensors, RGB cameras and RGB-D cameras.”
Unsupervised feature learning for 3D scene labeling (2014, methodology): “the output of 3D reconstructions techniques like Kinect Fusion [19] and RGB-D Mapping [20], [21].”
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels (2013, methodology): “Our system then uses these object annotations as constraints in a generalized bundle adjustment procedure (Section 5) to refine the camera pose.”
Discriminatively Trained Sparse Code Gradients for Contour Detection (2012, neutral): “Henry et. al. used RGB-D cameras to scan large environments into 3D models [10].”
Neural Autonomous Navigation with Riemannian Motion Policy (2019, background): “Although significant progress has been made [8], [9], [10], [11], these techniques still have trouble with textureless environments or fast-moving cameras.”
Robust odometry estimation for RGB-D cameras (2013, methodology): “Odometry methods based on visual features such as SIFT or SURF are too slow for being used in low-latency applications [6], [7].”
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery (2013, background): “, 2008) or improving camera pose estimation (Castaneda et al., 2011; Henry et al., 2010; Streckel et al., 2007).”
Real-time grasp detection using convolutional neural networks (2014, background): “Robotic systems increasingly leverage RGB-D sensors and data for tasks like object recognition [8], detection [9] [10], and mapping [11] [12].”