The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design

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3D Scene Understanding by Voxel-CRF (2013, methodology): “From the depth data and its projected point clouds, we can identify 3D surfaces [23] and these are useful to understand images for two reasons.”
Deep Learning for Robust Normal Estimation in Unstructured Point Clouds (2016, methodology): “Originally designed for simple 2D primitive extraction in images [TM78, Dav88, SW02], it has since then been used for various purposes from 3D primitive extraction [BELN11], recognition and classiﬁ-cation [KPW ∗ 10,PWP ∗ 11], to general model selection [Bal81].”
Multiview RGB-D Dataset for Object Instance Detection (2016, methodology): “A 3D Hough transform based plane detection algorithm is utilized to detect all planar surfaces in the 3D scene [4].”
Holistic Scene Understanding for 3D Object Detection with RGBD Cameras (2013, methodology): “We find them through 3D Hough transform [1] instead of object detection.”
Deep Learning Advances in Computer Vision with 3D Data (2017, methodology): “Furthermore, clustering algorithms [Douillard et al. 2011], 3D Hough Trans-form [Borrmann et al. 2011], RANSAC [Schnabel et al. 2007], and probabilistic models, such as Markov Random Fields (MRFs) and Conditional Random Fields (CRFs), have also been employed (e.g., Valentin et al. [2013]).”