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Point Cloud Compression Techniques

Main methods to implement point cloud compression

Radu Siniavschi

on 20 May 2014

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Transcript of Point Cloud Compression Techniques

Curve driven point
cloud coding
objects are scaned as grid patterns
predictive coder based on curve point representation
compresses the ordered points
compressed points are then puted on curves
Real-time Compression of Point Cloud Streams
compression framework can handle general point cloud streams of arbitrary and varying size, point order and point density.
a technique for comparing the octree data structures of consecutive point clouds.

encoding their structural differences, we can
successively extend the point clouds at the decoder
A point cloud is a set of
data points in some
coordinate system.
In a three-dimensional
coordinate system,
these points are usually
defined by X, Y, and Z coordinates, and often are intended to represent the external surface of an object.
3D Objects Compression Techniques
Adaptive aritmetic coding
Once point clouds have to be stored or transmitted over rate-limited communication channels, methods for compressing this kind of data become highly interesting.
Point clouds is a dense representation of points and curves so we need to compress this data to to take up less storage space. Point clouds also allows us to to capture images dynamic, in time and also in the X,Y,Z coordinates.
cloud coding
of Point
The main point cloud compression methods
the encoding process removes the statistical redundancy in the quantized absolute component of the residual by entropy Huffman coding
an encoder
a modulator

The object surface is sampled by a grid pattern formed by straight lines distinguishable only as horizontal and vertical lines
Unlikely Huffman coding that replaces each symbol by a variable-length code, arithmetic coding associates the entire curve C with a sub-interval [a, b) inside the original interval of [0, 1)
this model-based approach results in significant compression again. Specifically, an average bitrate reduction up to 7% are observed.
cloud coding
Adaptive aritmetic coding
Curve driven point
Real-time Compression of Point Cloud Streams
Experimental validation
Using the octree compression technique and applying the encoding and decoding on all 60 pictures, I manage to obtain an actual 3D image of the object after a couple of hours of rendering.
Like in the elephant case, I used a photo gallery of minimum 20 pictures of the same object from different angles.
Single point clouds as well as streams of points clouds can be efficiently compressed. In the presented example, I capture point clouds with the OpenNIGrabber to be compressed using the PCL point cloud compression techniques. The input images are taken from a Microsoft Kinect Device.
Thank you !
Radu Siniavschi
Prof. Dr. Ing. Mihai Caramihai

But what should we do about
this large set of data ?
I fisrt created a mesh of the little Buddha
And then I generated the first point clouds with MeshLab
Full transcript