Fast range image segmentation using high-level segmentation primitives

Jiang, Xiaoyi; Meier, U.; Bunke, Horst

doi:10.1109/acv.1996.572006

Cited by 41 publications

(26 citation statements)

References 15 publications

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“…The remaining points in the cloud are voxelized and clustered into distinct blobs representing object candidates. This model-based approach does not require prior knowledge about the environment such as neighboring information and the number of regions to process [21,23].…”

Section: B Object Segmentation Into Point Cloud: Representation Of Smentioning

confidence: 99%

A Probabilistic Approach to Unsupervised Induction of Combinatory Categorial Grammar in Situated Human-Robot Interaction

Aly

Taniguchi

Mochihashi

2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

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Robots are progressively moving into spaces that have been primarily shaped by human agency; they collaborate with human users in different tasks that require them to understand human language so as to behave appropriately in space. To this end, a stubborn challenge that we address in this paper is inferring the syntactic structure of language, which embraces grounding parts of speech (e.g., nouns, verbs, and prepositions) through visual perception, and induction of Combinatory Categorial Grammar (CCG) in situated humanrobot interaction. This could pave the way towards making a robot able to understand the syntactic relationships between words (i.e., understand phrases), and consequently the meaning of human instructions during interaction, which is a future scope of this current study.

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Section: B Object Segmentation Into Point Cloud: Representation Of Smentioning

confidence: 99%

A Probabilistic Approach to Unsupervised Induction of Combinatory Categorial Grammar in Situated Human-Robot Interaction

Aly

Taniguchi

Mochihashi

2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

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“…22,23] from the vision and robotics communities. Normal computation at each LiDAR point is the first step in most range segmentation algorithms [15,16,17], which is also a crucial step for precise region extraction.…”

Section: Related Workmentioning

confidence: 99%

Window detection from mobile LiDAR data

Wang¹,

Bach²,

Ferrie

2011

2011 IEEE Workshop on Applications of Computer Vision (WACV)

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We present an automatic approach to window and façade detection from LiDAR (Light Detection And Ranging) data collected from a moving vehicle along streets in urban environments. The proposed method combines bottom-up with top-down strategies to extract façade planes from noisy LiDAR point clouds. The window detection is achieved through a two-step approach: potential window point detection and window localization. The facade pattern is automatically inferred to enhance the robustness of the window detection. Experimental results on six datasets result in 71.2% and 88.9% in the first two datasets, 100% for the rest four datasets in terms of completeness rate, and 100% correctness rate for all the tested datasets, which demonstrate the effectiveness of the proposed solution. The application potential includes generation of building facade models with street-level details and texture synthesis for producing realistic occlusion-free façade texture.

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“…But a more difficult problem is to find all possible planes in a scene, and this is the main target of this paper. It is a very similar problem to color image segmentation, but the input now is the 3D point locations, which has been deployed in various approaches, such as depth discontinuity [2,35], local surface normal similarity [30,40] and scanline grouping [17,26,18]. Algorithm-wise, although all above approaches deploy different methods to handle minor sensor noise, such as consensus [34] and voting [5], and perform well when the dataset is of reasonable quality, they still fall into the category of deterministic methods.…”

Section: Problem Overview and Related Workmentioning

confidence: 99%

Simultaneous image segmentation and 3D plane fitting for RGB-D sensors — An iterative framework

Guan

et al. 2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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In this paper, we segment RGB-D sensor (e.g. Microsoft Kinect camera) images into 3D planar surfaces. We initialize a set of plane equations based solely from the depth (point cloud) information. We then iteratively refine the pixel-to-plane assignment and plane equations. During this process, the number of planes are also reduced by merging adjacent local planes with similar orientations. For the pixel-to-plane assignment, we treat the image as a Markov Random Field (MRF), and solve the association problem using graph-based global energy minimization. We design the energy terms to encapsulate both appearance cues from the RGB (color) channels and shape cues from the D (depth) channel. Experiments show that the use of both appearance and geometry information significantly improves the segmentation quality, especially so at genuine plane edges and plane intersections. As a byproduct, the framework also automatically fills in missing depth information.

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Fast range image segmentation using high-level segmentation primitives

Cited by 41 publications

References 15 publications

A Probabilistic Approach to Unsupervised Induction of Combinatory Categorial Grammar in Situated Human-Robot Interaction

A Probabilistic Approach to Unsupervised Induction of Combinatory Categorial Grammar in Situated Human-Robot Interaction

Window detection from mobile LiDAR data

Simultaneous image segmentation and 3D plane fitting for RGB-D sensors — An iterative framework

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Fast range image segmentation using high-level segmentation primitives

Cited by 41 publications

References 15 publications

A Probabilistic Approach to Unsupervised Induction of Combinatory Categorial Grammar in Situated Human-Robot Interaction

A Probabilistic Approach to Unsupervised Induction of Combinatory Categorial Grammar in Situated Human-Robot Interaction

Window detection from mobile LiDAR data

Simultaneous image segmentation and 3D plane fitting for RGB-D sensors &#x2014; An iterative framework

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Simultaneous image segmentation and 3D plane fitting for RGB-D sensors — An iterative framework