Building LOD Representation for 3D Urban Scenes

TL;DR

This paper introduces a novel algorithm for generating robust, semantically meaningful level-of-detail representations of 3D urban scenes, improving visualization and interaction on resource-limited devices.

Contribution

The paper presents an innovative LOD-Tree structure that groups planar primitives into meaningful 3D structures for better LOD generation in urban scenes.

Findings

Effective LOD representations for complex urban scenes

Robustness against noise in reconstructed models

Semantic preservation in LOD generation

Abstract

The advances in 3D reconstruction technology, such as photogrammetry and LiDAR scanning, have made it easier to reconstruct accurate and detailed 3D models for urban scenes. Nevertheless, these reconstructed models often contain a large number of geometry primitives, making interactive manipulation and rendering challenging, especially on resource-constrained devices like virtual reality platforms. Therefore, the generation of appropriate levels-of-detail (LOD) representations for these models is crucial. Additionally, automatically reconstructed 3D models tend to suffer from noise and lack semantic information. Dealing with these issues and creating LOD representations that are robust against noise while capturing the semantic meaning present significant challenges. In this paper, we propose a novel algorithm to address these challenges. We begin by analysing the properties of planar primitives detected from the input and group these primitives into multiple level sets by forming meaningful 3D structures. These level sets form the nodes of our innovative LOD-Tree. By selecting nodes at appropriate depths within the LOD-Tree, different LOD representations can be generated. Experimental results on real and complex urban scenes demonstrate the merits of our approach in generating clean, accurate, and semantically meaningful LOD representations.