What are the two methods that subdivide the bounding box?

Two methods that recursively subdivide the bounding box are the kd-tree approach of Devillers and Gandoin [2000] and the oct-tree approach of Peng and Kuo [2003].

What is the way to build a prediction tree?

For construction the authors investigated two build orders: sorted along a coordinate axis or sorted in an approximate geodesic order from the first point.

Open AccessProceedings Article10.1145/1187112.1187277

Predictive point-cloud compression

Stefan Gumhold, +3 more

- 31 Jul 2005

- pp 137

139

TL;DR: R Rendering directly with points eliminates the complex task of reconstructing a surface and allows handling of non-surfaces like models such as trees.

Abstract: Point clouds have recently become a popular alternative to polygonal meshes for representing three-dimensional geometric models. 3D photography and scanning systems acquire the geometry and appearance of real-world objects in form of point samples. Rendering directly with points eliminates the complex task of reconstructing a surface and allows handling of non-surfaces like models such as trees. With modern acquisition techniques producing larger and larger amounts of points, efficient schemes for compressing such data have become necessary.

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Most frequently asked questions

1. What are the contributions mentioned in the paper "Predictive point-cloud compression" ?

In this paper, the authors proposed a method for compressing point clouds using a binary tree over the points by pairing close-by points.

2. What is the main purpose of a point cloud?

Rendering directly with points eliminates the complex task of reconstructing a surface and allows handling of non-surfaces like models such as trees.

3. What is the drawback of these methods?

One drawback of these spatial subdivision methods is that they do not generalize to include attribute data such as normals and colors.

4. What is the coding of the tree?

The user can choose among two prediction rules: 1) in constant prediction (con) the point ~pi is predicted from the parent position ~ppred =~pi−1, 2) in linear prediction (lin) also the grandparent point ~pi−2 is used according to ~ppred := 2~pi−1−~pi−2.Construction of Prediction Tree: Inspired by Kronrod and Gotsman [2002], the authors build a prediction tree that minimizes the residuals, i.e. the lengths of the corrective vectors.

Figure 1: prediction trees built with a) constant prediction (maple01 has no fine-scale structure) and b) linear prediction (bunny is a 3d scan); c) comparison of compression rates in bits per point (bpp) after uniform quantization to 12 bits per coordinate, i.e. 36 bpp uncompressed data

Citations

•Proceedings Article•10.5555/2386388.2386404

Octree-based point-cloud compression

Ruwen Schnabel, +1 more

- 29 Jul 2006

TL;DR: A progressive compression method for point sampled models that is specifically apt at dealing with densely sampled surface geometry and it is demonstrated that additional point attributes, such as color, can be well integrated and efficiently encoded in this framework.

...read moreread less

496

Proceedings Article•10.1109/ICRA.2012.6224647

Real-time compression of point cloud streams

Julius Kammerl, +5 more

- 14 May 2012

TL;DR: This work presents a novel lossy compression approach for point cloud streams which exploits spatial and temporal redundancy within the point data and presents a technique for comparing the octree data structures of consecutive point clouds.

...read moreread less

425

•Journal Article•10.1109/TVCG.2007.70441

A Generic Scheme for Progressive Point Cloud Coding

Yan Huang, +3 more

- 01 Mar 2008

- IEEE Transactions on Visualization and C...

TL;DR: A generic point cloud encoder is proposed that provides a unified framework for compressing different attributes of point samples corresponding to 3D objects with an arbitrary topology and employs attribute-dependent encoding techniques to exploit the different characteristics of various attributes.

...read moreread less

230

Proceedings Article•10.1145/3343031.3351061

3D Point Cloud Geometry Compression on Deep Learning

Tianxin Huang, +1 more

- 15 Oct 2019

TL;DR: A new 3D point cloud geometry compression method based on deep learning, also an auto-encoder performing better than other networks in detail reconstruction, which can reach much higher compression ratio than the state-of-art while keeping tolerable loss.

...read moreread less

189

•Proceedings Article•10.1145/3329714.3338130

3D Point Cloud Compression: A Survey

Chao Cao, +2 more

- 26 Jul 2019

TL;DR: A comprehensive overview of the3D-PCC state-of-the-art methods is proposed, including 1D traversal compression, 2D-oriented techniques, which take leverage of existing 2D image/video compression technologies and finally purely 3D approaches, based on a direct analysis of the 3D data.

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166

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References

Proceedings Article•10.1145/237170.237216

Progressive meshes

Hugues Hoppe

- 01 Aug 1996

TL;DR: The progressive mesh (PM) representation is introduced, a new scheme for storing and transmitting arbitrary triangle meshes that addresses several practical problems in graphics: smooth geomorphing of level-of-detail approximations, progressive transmission, mesh compression, and selective refinement.

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Patent

Triangle mesh compression

Costa Touma, +1 more

- 30 Apr 1998

TL;DR: In this paper, a method for compressing a mesh having a plurality of vertices, each vertex characterized by a degree equal to the number of edges incident thereon, was proposed.

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672

Proceedings Article•10.1145/280814.280836

Real time compression of triangle mesh connectivity

Stefan Gumhold, +1 more

- 24 Jul 1998

TL;DR: A new compressed representation for the connectivity of a triangle mesh is introduced allowing a possible hardware realization of the decompression algorithm which could significantly increase the rendering speed of pipelined graphics hardware.

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•Book Chapter•10.1007/3-540-26808-1_1

Recent advances in compression of 3D meshes

Pierre Alliez

- 08 Sep 2005

TL;DR: Recent developments in compression of 3D surface meshes are surveyed and some open questions and directions for future research are listed.

...read moreread less

331

•Proceedings Article•10.1109/VISUAL.2000.885711

Geometric compression for interactive transmission

Devillers, +1 more

- 01 Jan 2000

Predictive point-cloud compression

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Most frequently asked questions

1. What are the contributions mentioned in the paper "Predictive point-cloud compression" ?

2. What is the main purpose of a point cloud?

3. What is the drawback of these methods?

4. What is the coding of the tree?

5. What are the advantages of point clouds?

6. What are the two methods that subdivide the bounding box?

7. What is the way to build a prediction tree?

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Citations

Octree-based point-cloud compression

Real-time compression of point cloud streams

A Generic Scheme for Progressive Point Cloud Coding

3D Point Cloud Geometry Compression on Deep Learning

3D Point Cloud Compression: A Survey

References

Progressive meshes

Triangle mesh compression

Real time compression of triangle mesh connectivity

Recent advances in compression of 3D meshes

Geometric compression for interactive transmission

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