# Voxelisation Algorithms and Data Structures: A Review

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Voxelisation Properties

^{3}be the subset of 3D Euclidian space R

^{3}that is represented by all points whose coordinates are integers. This subset is called a grid [27]. A grid point represents a cell commonly referred to as a voxel. Voxels can have multiple properties, which can be organised differently with respect to the application. Binary voxelisation is a term to indicate that a voxel can have a property, which can take only two values: 0 (empty) or 1 (filled).

#### 2.1. Common Voxelisation Properties

#### 2.2. Binary and Non-Binary Voxelisation

## 3. Voxelisation of 3D Geometric Primitives

#### 3.1. Point Voxelisation

#### 3.2. Line Voxelisation

#### 3.2.1. 6-Connected Voxelisation Algorithms

#### 3.2.2. 26-Connected Voxelisation Algorithms

#### 3.2.3. Spline Voxelisation Algorithms

#### 3.2.4. Comparison of Line Voxelisation Algorithms

#### 3.3. Triangle Voxelisation

#### 3.3.1. Rasterisation

#### 3.3.2. Raycasting

#### 3.3.3. Comparison of Triangle Voxelisation Algorithms

#### 3.4. Surface Voxelisation

#### 3.4.1. Slice-Based

#### 3.4.2. Rasterisation

#### 3.4.3. Comparison of Surface Voxelisation Algorithms

#### 3.5. Solid Voxelisation

#### 3.5.1. Slice-Based

#### 3.5.2. Rasterisation

#### 3.5.3. Comparison of Solid Voxelisation Algorithms

## 4. Voxel Data Technology and Structures

#### 4.1. Voxel Hardware Technology

#### 4.2. Voxel Data Structures

#### 4.2.1. Static Grids

^{3}scene size, a required memory on a GPU is 945MB, which is substantially less compared to an SVO requiring 5.1GB without even counting pointers. Considering this technique, a symmetrically-aware sparse voxel directed acyclic graph (SSVDAG) is presented suggesting memory compression of nearly two times as opposed to SVDAG [95,99]. This method in addition to the original SVDAG method for nodes that are identified as similar creates tagged pointers on the level above which keep the transformation that needs to be applied to recover the original subtree, and compact similar nodes based on their occurrence frequency. By compressing arbitrary data such as colour, vectors normal and reflectance information apart from the geometry different methods are proposed [100,101,102].

#### 4.2.2. Dynamic Grids

^{3}bricks to represent leaf nodes, where the grid size does not have to be predefined. This approach is extended afterwards with dynamic topology update for fluid simulations being able to deal with tens of millions of particles (https://github.com/NVIDIA/gvdb-voxels (accessed on 25 September 2021)) [109]. For physically and topologically complex material point method (MPM) simulations harnessing the power of GPUs and SPGrid, highly parallelised data structure is presented dealing with millions of particles [110]. To create a more robust method for high-performance computations on spatially sparse data structures Taichi language is proposed [111], which is open source (https://github.com/yuanming-hu/taichi (accessed on 25 September 2021)) as well. Apart from the high achievable performance, this language is greatly extendible and easy to learn to support different simulation demands. This programming language allows selecting between VDB, GSPGrid or even custom-based sparse data structure. Recently, NanoVDB data structure is introduced as a linearised version of an OpenVDB data structure [105], with several advantages including the use of GPUs, a stand-alone raytracer that is compatible with most graphics APIs, being fast and efficient in copying data between devices (e.g., CPU and GPU) and randomly accessing voxels, etc.

## 5. Conclusions and Future Works

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**The 26 neighbours of a voxel; six voxels sharing face (in red), 12 voxels sharing edge (in green) and 8 voxels sharing a vertex (in blue).

**Figure 2.**Connectivity and separation in 2D voxel set representing a circle. On the left, 4-connected voxelisation is achieved, which is at the same time 8-separating. On the right, 8-connected, and thus, 4-separating voxelisation is presented.

**Figure 3.**Representation of a cover (

**a**), supercover (

**b**), partial cover (

**c**), and partial cover (

**d**) well-voxelised curve in 2D.

**Figure 4.**Intersection points of lines in 2D space: ‘tunnelling’, i.e., an intersection point is missing in 8-connected (

**a**) and two voxels (in dark) as an intersection point (

**b**).

**Figure 5.**(

**a**) RLV or SLV; (

**b**) SLV generates more voxels than RLV considering all touching voxels; (

**c**,

**d**) small variations of the voxels coverage generated by Tripod and ILV algorithm.

**Figure 6.**Voxelisation of a curve using intersecting targets in 2D. (

**a**) Using cross-diagonal intersection targets forming 4-connected and 8-separating voxelisation. (

**b**) Using crosshairs intersection targets forming 8-connected and 4-separating voxelisation.

**Figure 7.**Triangle voxelisation. (

**a**) uniform ray casting (

**b**) rasterisation techniques; in red, the bounding box, in green tiles that are outside; in purple tiles that are inside; in yellow tiles that are intersecting the triangle are presented.

**Figure 8.**Limitations of one-side slicing seen from the bottom up. (

**a**) view frustum of a perspective camera where the left object is completely missed, and the right object has disconnected voxels (

**b**) depth slice of an orthogonal camera.

**Figure 9.**Different 3D solids. None-watertight models: enclosed object (

**a**), object with an inner wall (

**b**). Watertight solid models: enclosed object (

**c**), object with a hole (

**d**).

Method | Type | Property | General Purpose |
---|---|---|---|

2D Bresenham’s line algorithm [58] | Integer-only | 8-connected | Line primitives rasterisation |

2D-DDA | Floating-point or integer | 8-connected | Line primitives rasterisation |

3D-DDA [59] | Floating-point or integer | 26-connected | Line primitives voxelisation |

RLV & SLV [1] | Floating-point | Conservative | Line primitives voxelisation |

Xiaolin Wu’s line algorithm [57] | Floating-point | Conservative | Antialiasing |

Tripod [23] | Integer | 6-connected | Line primitives voxelisation |

3D Bresenham’s line algorithm [60] | Integer-only | 26-connected | Line primitives voxelisation |

Targets-based approaches [61] | Floating-point | 6/26-connected | Irregular line primitives voxelisation |

ILV [41] | Integer-only | 6-connected | Surface voxelisation |

Method | Type | Property | Main Technique |
---|---|---|---|

[3,63,65,66,67,71] | Rasterisation | 6/26-connected & conservative | Bounding box, backtrack, zigzag, central-line, and midpoint traversal |

[3,26,68,69,70] | Rasterisation | 6/26-connected & conservative | Tile-based |

[55,73,74,75,77] | Raycasting | 6/26-connected | Ray-triangle and ray-polygon intersection |

[41,62] | Rasterisation | 6/26-connected & conservative | Line rasterisation |

Method | Type | Property | Main Technique | General Purpose |
---|---|---|---|---|

[43] | Slice-based | ‘26-connected’ | Plane slicing | Rendering |

[14] | Slice-based | ‘26-connected’ | Depth peeling | Rendering |

[38] | Slice-based | Conservative | Bounding box | Collision detection |

[72] | Rasterisation | 26-connected | Bounding box | Rendering |

[79] | Rasterisation | 26-connected | Bounding box | Rendering |

[81] | Rasterisation | 26-connected | Tile-based | Voxelisation |

[3] | Rasterisation | Conservative & 26-connected | Bounding box | Voxelisation |

[83] | Rasterisation | ‘Conservative’ | Two level grids | Rendering |

[26] | Rasterisation | Conservative & 26-connected | Tile-based & bucketing | Voxelisation & rendering |

[40] | Rasterisation | 26-connected | Point tessellation | Voxelisation |

[55] | Raycasting | 6/26-connected | Intersecting targets | Voxelisation |

[41] | Rasterisation | 6/26-connected | ILV | Voxelisation |

[45] | Rasterisation & raycasting | Conservative | Tile-based + ray-triangle intersection | Voxelisation & rendering |

Method | Type | Property | Main Technique | General Purpose |
---|---|---|---|---|

[43] | Slice-based | Interior only | Plane slicing | Voxelisation |

[84] | Slice-based | Interior only | Surface voxelisation + 2D scan-filling | Voxelisation |

[2] | Slice-based | Interior only | Bitwise OR operation | Rendering |

[28] | Slice-based | Interior only | Mask creation | Voxelisation |

[39] | Slice-based | Interior only & conservative | Single pass & bitwise OR operation | Voxelisation |

[3] | Rasterisation | Interior only | Tile-based, bounding box, sparse octree | Voxelisation & storage |

Method | Geometry Voxelisation Type | GPU API/CPU | Voxel Data Structure | Attribute Conservation | Out-of-Core |
---|---|---|---|---|---|

[92] | Any | CPU | Regular grid | x | x |

[39] | Solid | OpenGL & DirectX 10 | Regular grid | x | - |

[28] | Solid | OpenGL 2 | Regular grid | x | - |

[3] | Solid | CUDA | SVO | - | - |

[93,94] | Surface | CUDA | SVO | x | - |

[26] | Surface | CUDA | Regular grid | x | - |

[40] | Surface | OpenGL 4 & DirectX 11 | Regular grid | x | - |

[103] | Surface | DirectX 11 | SVO | x | - |

[96] | Surface | CPU | SVO | - | x |

[98] | Surface | CUDA | SVDAG | - | x |

[104] | Surface | CUDA | SVO | x | x |

[95,99] | Surface | OpenGL | SSVDAG | - | x |

[100] | Surface | GPU | SVDAG | x | x |

[101,102] | Surface | CUDA | SVDAG | x | x |

Method | GPU API/CPU | Voxel Data Structure | Out-of-Core | Maximum Tested Grid Size | General Purpose |
---|---|---|---|---|---|

[30] | CPU | SBG | - | 2000 ^{3} | Simulation and rendering |

[106] | CPU | RLE | - | 624 × 554 × 488 | Rendering |

[32] | CPU | DT-Grid | - | 1024 ^{3} | Fluid simulation |

[31] | CPU | H-RLE | - | 5K × 3K × 3K | Fluid simulation |

[33] | CPU | VDB | x | 15K × 900 × 500 | Simulation and rendering |

[34] | CPU | SPGrid | - | 2K × 2K × 4K | Fluid simulation |

[108] | CUDA | GVDB | - | 2048 ^{3} | Simulation and rendering |

[109] | CUDA | GVDB | - | 1056 × 288 × 768 | Fluid simulation |

[110] | CUDA | GSPGrid | - | 512 ^{3} | MPM simulation |

[111] | CUDA, OpenGL, Apple Metal | GVDB, GSPGrid, custom | - | 4096 ^{3} | Simulation, rendering, and 3D deep learning |

[105] | CUDA, OpenCL, OptiX OpenGL, DirectX12, WebGL, HLSL & GLSL | NanoVDB | x | / | Simulation and rendering |

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## Share and Cite

**MDPI and ACS Style**

Aleksandrov, M.; Zlatanova, S.; Heslop, D.J. Voxelisation Algorithms and Data Structures: A Review. *Sensors* **2021**, *21*, 8241.
https://doi.org/10.3390/s21248241

**AMA Style**

Aleksandrov M, Zlatanova S, Heslop DJ. Voxelisation Algorithms and Data Structures: A Review. *Sensors*. 2021; 21(24):8241.
https://doi.org/10.3390/s21248241

**Chicago/Turabian Style**

Aleksandrov, Mitko, Sisi Zlatanova, and David J. Heslop. 2021. "Voxelisation Algorithms and Data Structures: A Review" *Sensors* 21, no. 24: 8241.
https://doi.org/10.3390/s21248241