Large scale 3 d point cloud compression using adaptive radial distance prediction in hybrid coordinate domains

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Large scale 3 d point cloud compression using adaptive radial distance prediction in hybrid coordinate domains +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com ieee projects chennai, ieee projects bangalore

Large-Scale 3D Point Cloud Compression Using Adaptive Radial
Distance Prediction in Hybrid Coordinate Domains
Abstract:
An adaptive range image coding algorithm for the geometry compression
of large-scale 3D point clouds (LS3DPCs) is proposed in this work. A
terrestrial laser scanner generates an LS3DPC by measuring the radial
distances of objects in a real world scene, which can be mapped into a
range image. In general, the range image exhibits different characteristics
from an ordinary luminance or color image, and thus the conventional
image coding techniques are not suitable for the range image coding. We
propose a hybrid range image coding algorithm, which predicts the radial
distance of each pixel using previously encoded neighbors adaptively in
one of three coordinate domains: range image domain, height image
domain, and 3D domain. We first partition an input range image into
blocks of various sizes. For each block, we apply multiple prediction modes
in the three domains and compute their rate-distortion costs. Then, we
perform the prediction of all pixels using the optimal mode and encode the
resulting prediction residuals. Experimental results show that the proposed
algorithm provides significantly better compression performance on
various range images than the conventional image or video coding
techniques.

Existing System:
A highly detailed point cloud can be captured with a small scale laser
scanner, which samples points on the surface of a single object. On the
other hand, a large-scale terrestrial laser scanner based on the light
detection and ranging (LIDAR) can generate a point cloud for a real-world
scene including many objects, facilitating many applications of geometry
signal processing.
Proposed System:
We propose a novel geometry compression algorithm for LS3DPCs. Since
an LS3DPC can be converted into a range image, in which the horizontal
and vertical coordinates of pixels correspond to the azimuthal and polar
angles of 3D points, we encode the radial distances in the range image
instead of the 3D coordinates of the point cloud.
We also introduce an alternative representation of range image, called
height image, which stores the height values of 3D points that are uniquely
generated from a given range image. Then, we can encode the radial
distances in the height image domain as well. In practice, we first divide an
input range image adaptively into blocks of various sizes.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.

• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server

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Large scale 3 d point cloud compression using adaptive radial distance prediction in hybrid coordinate domains