Space- and Time-Efficient Storage of LiDAR Point Clouds

TL;DR

This paper introduces a new compact data structure for storing LiDAR point clouds that enhances query efficiency and indexing capabilities compared to existing LAZ format, addressing current limitations in large dataset management.

Contribution

The authors propose a novel data structure that improves storage efficiency and query performance for LiDAR point clouds over existing methods like LAZ.

Findings

Achieves faster query times for LiDAR data

Provides superior indexing capabilities

Reduces storage space compared to LAZ format

Abstract

LiDAR devices obtain a 3D representation of a space. Due to the large size of the resulting datasets, there already exist storage methods that use compression and present some properties that resemble those of compact data structures. Specifically, LAZ format allows accesses to a given datum or portion of the data without having to decompress the whole dataset and provides indexation of the stored data. However, LAZ format still have some drawbacks that should be faced. In this work, we propose a new compact data structure for the representation of a cloud of LiDAR points that supports efficient queries, providing indexing capabilities that are superior to those of LAZ format.