In recent years, 3D point cloud has gained increasing attention as a new representation for objects However, the raw point cloud is often noisy and contains outliers Therefore, it is crucial to remove the noise and outliers from the point cloud while preserving the features, in particular, its fine details This paper makes an attempt to present a comprehensive analysis of the state-of-the-art methods for filtering point cloud The existing methods are categorized into seven classes, which concentrate on their common and obvious traits An experimental evaluation is also performed to demonstrate robustness, effectiveness and computational efficiency of several methods used widely in practice

A review of algorithms for filtering the 3D point cloud

State of research in automatic as-built modelling

To reduce the cost of storing, processing, and visualizing a large-scale point cloud, we propose a randomized resampling strategy that selects a representative subset of points while preserving application-dependent features. The strategy is based on graphs, which can represent underlying surfaces and lend themselves well to efficient computation. We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling; by minimizing the error, we obtain a general form of optimal resampling distribution. The proposed resampling distribution is guaranteed to be shift-, rotation- and scale-invariant in the three-dimensional space. We then specify the feature-extraction operator to be a graph filter and study specific resampling strategies based on all-pass, low-pass, high-pass graph filtering and graph filter banks. We validate the proposed methods on three applications: Large-scale visualization, accurate registration, and robust shape modeling demonstrating the effectiveness and efficiency of the proposed resampling methods.

Fast Resampling of Three-Dimensional Point Clouds via Graphs

Markerless Motion Capture with unsynchronized moving cameras

Fully automated registration of 3D data to a 3D CAD model for project progress monitoring

Special Section: Point-Based Graphics: Processing and interactive editing of huge point clouds from 3D scanners

This paper describes a new out-of-core multi-resolution data structure for real-time visualization and interactive editing of large point clouds. In addition, an editing system is discussed that makes use of the novel data structure to provide interactive editing tools for large scanner data sets. The new data structure provides efficient rendering and allows for handling very large data sets using out-of-core storage. Unlike related previous approaches, it also provides dynamic operations for online insertion, deletion and modification of points with time mostly independent of scene complexity. This permits local editing of huge models in real time while maintaining a full multi-resolution representation for visualization. The data structure is used to implement a prototypical editing system for large point clouds. It provides real-time local editing tools for huge data sets as well as a two-resolution scripting mode for planning large, non-local changes which are subsequently performed in an externally efficient offline computation. We evaluate our implementation on several synthetic and real-world examples of sizes up to 63GB.

/pdf/interactive-editing-of-large-point-clouds-9fmlct32rs.pdf

Interactive Editing of Large Point Clouds

This paper describes a new out-of-core multi-resolution data structure for real-time visualization and interactive editing of large point clouds In addition, an editing system is discussed that makes use of the novel data structure to provide interactive editing tools for large scanner data sets The new data structure provides efficient rendering and allows for handling very large data sets using out-of-core storage Unlike related previous approaches, it also provides dynamic operations for online insertion, deletion and modification of points with time mostly independent of scene complexity This permits local editing of huge models in real time while maintaining a full multi-resolution representation for visualization The data structure is used to implement a prototypical editing system for large point clouds It provides real-time local editing tools for huge data sets as well as a two-resolution scripting mode for planning large, non-local changes which are subsequently performed in an externally efficient offline computation We evaluate our implementation on several synthetic and real-world examples of sizes up to 63GB

Interactive Editing of Large Point Clouds.

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Special Section: Point-Based Graphics: Processing and interactive editing of huge point clouds from 3D scanners

Interactive Editing of Large Point Clouds

Interactive Editing of Large Point Clouds.