Semantic Labeling of 3D Point Clouds for Indoor Scenes

By Hema Swetha Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena. 2011

424 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, 3D Vision & Pose

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Citations in the corpus

Learning Rich Features from RGB-D Images for Object Detection and Segmentation (2014, background): “There are multiple ways to approach this problem, like that of doing a bottom-up segmentation and classifying the resulting superpixels [18,30] or modeling contextual relationships among pixels and superpixels [23,33].”
RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “The curves under the images are the accelerometer data at 50 Hz of devices attached to the knife, the mixing spoon, the small spoon, the peeler, the glass, the oil bottle, and the pepper dispenser mentioned datasets above, another 26 extra RGB-D datasets for different applications are also added int”
Learning human activities and object affordances from RGB-D videos (2012, background): “…indoor environments (Henry et al., 2012), semantic labeling of environments by modeling objects and their relations to other objects in the scene (Koppula et al., 2011; Lai et al., 2011b; Rosman and Ramamoorthy, 2011; Anand et al., 2012), developing frameworks for object recognition and pose…”
Deep learning for detecting robotic grasps (2013, methodology): “These are computed after the image is aligned to the gripper so that they are always relative to the gripper plates.”
Cognitive Mapping and Planning for Visual Navigation (2017, background): “Semantics have also been associated with 3D environments more generally (Koppula et al. 2011; Gupta et al. 2013).”
Joint 2D-3D-Semantic Data for Indoor Scene Understanding (2017, background): “The Computer Vision community has been affected by this change and is experiencing a lot of development in data-driven 2.5D and 3D vision [6, 17, 18, 19, 1].”
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels (2013, methodology): “There are several 3D datasets that capture full videos [20, 21, 12, 2, 7] rather than snapshots [11].”
Unstructured human activity detection from RGBD images (2011, methodology): “We have also made the dataset and code available open-source at: http://pr.cs.cornell.edu/humanactivities”
Robust real-time visual odometry for dense RGB-D mapping (2013, methodology): “Exploiting physical and contextual relationships between objects and the environment is key to achieve a high precision in semantic tasks such as segmentation [15, 22, 29] and detection [9, 8].”
Sliding Shapes for 3D Object Detection in Depth Images (2014, methodology): “Semantic segmentation: A popular way of formulating object recognition in 3D is to predict the semantic label for each region of a depth map or 3D mesh [37, 38, 11, 10, 39–44].”
Convolutional-Recursive Deep Learning for 3D Object Classification (2012, methodology): “Most recent methods for object recognition with RGB-D images use hand-designed features such as SIFT for 2d images [2], Spin Images [3] for 3D point clouds, or specific color, shape and geometry features [4, 5].”
SemanticFusion: Dense 3D semantic mapping with convolutional neural networks (2016, methodology): “The majority of other approaches to indoor semantic labelling either focuses on offline batch mapping methods [24], [11] or on single-frame 2D segmentations which do not aim to produce a semantically annotated 3D map [3], [20], [15], [22].”
Aligning 3D models to RGB-D images of cluttered scenes (2015, background): “There have been corresponding works for RGB-D images studying the problems of object detection [4, 13, 17, 20, 21, 22, 24, 34, 35, 36], semantic segmentation [3, 12, 13, 19, 25, 32, 33], and more recently instance segmentation [13, 33].”