Cherry: An Efficient Octree Serialization for Point Clouds
Understanding the significance and relevance of superkkt's GitHub project, "Cherry," requires an appreciation for the increasing implementation and applicability of point cloud data. A point cloud is a set of data points in space, typically produced by 3D scanners, and applications are plentiful, spanning from autonomous driving to 3D city modeling. Cherry, as an open-source project, presents a pragmatic and efficient mechanism for Octree serialization of point clouds, offering a crucial resource to those operating within these domains.
3D Scanners, Point Cloud Data, Octree Serialization, Autonomous Driving, 3D City Modeling, Open-source Project, Efficient Serialization, Octrees, Cherry, GitHub Project
Project Overview:
The central objective for the Cherry project is the efficient serialization of Octrees, specifically for point cloud data. Octree is a tree data structure where each internal node has exactly eight children. In point cloud data applications, Octrees are used to render complex 3D spaces more manageable. Cherry seeks to streamline the Octree serialization process by reducing the size of the serialized data, consequently improving the speed of processing.
Project Features:
Cherry's main feature is its lean and efficient serialization of Octrees. It accomplishes this through two primary methods:
a. Quantization: Cherry quantifies the Octree nodes’ location. The locational data is then represented in significantly smaller data using Octree representation, leading to leaner mesages and faster processing speeds.
b. Variable-Length Encoding: Cherry uses Variable-Length Encoding (VLE) to further compress the serialized data. With VLE, the shorter values of quantized displacement use fewer bits, compressing the data further.
Technology Stack:
Cherry is written in C++ and uses Google's Protocol Buffers (protobufs). Protobufs was a strategic technology choice, as it changes the way developers define data structures, known as message types. These message types can then be compiled by the Protocol Buffer compiler for the best efficiency in the chosen language (C++ in Cherry's case).
Project Structure and Architecture:
Cherry molds around a simple yet effective algorithm. It first quantizes node displacement to an unsigned short integer. Following quantization, Cherry introduces variable-length encoding for further compression. The structure of this project is simple but high performing. Due to its simplicity, it can be quickly adopted and adapted in projects requiring Octree serialization.
