Rendering massive indoor point clouds in virtual reality

Abstract

This paper addresses the challenges of rendering massive indoor point clouds in Virtual Reality. In these kind of visualizations the point of view is never static, imposing the need of a one-shot (i.e. non-iterative) rendering strategy, in contrast with progressive refinement approaches that assume that the camera position does not change between most consecutive frames. Our approach benefits from the static nature of indoor environments to pre-compute a visibility map that enables us to boost real-time rendering performance. The key idea behind our visibility map is to exploit the cluttered topology of buildings in order to effectively cull the regions of the space that are occluded by structural elements such as walls. This does not only improve performance but also the visual quality of the final render, allowing us to display in full detail the space and preventing the user to see the contiguous spaces through the walls. Additionally, we introduce a novel hierarchical data structure that enables us to display the point cloud with a continuous level of detail with a minimal impact on performance. Experimental results show that our approach outperforms state-of-the-art techniques in complex indoor environments and achieves comparable results in outdoor ones, proving the generality of our method.

Fusion of Thermal Point Cloud Series of Buildings for Inspection in Virtual Reality

“…The first one applies the proposal of [24] and performs the weighted mixing between the property value and the grayscale conversion of the color or texture of the point cloud. Specifically, the resulting color of each point, (𝑟 𝑚𝑖𝑥_𝑔𝑟𝑎𝑦 , 𝑔 𝑚𝑖𝑥_𝑔𝑟𝑎𝑦 , 𝑏 𝑚𝑖𝑥_𝑔𝑟𝑎𝑦 ), in the proposed new scale is: where 𝑟 𝑡 , 𝑔 𝑡 and 𝑏 𝑡 are obtained from equations ( 12) to (15), γ is a factor between 0 and 1 that allows the brightness to be adjusted, 𝐾() is the function that converts the original color 𝑟, 𝑔, 𝑏 to grayscale, and w(t) is the following weighted function:…”

“…where 𝑟 𝑡 , 𝑔 𝑡 and 𝑏 𝑡 are obtained from equations ( 12) through (15), and Ε is a mixing factor between 0 and 1. A slider bar is included in the interface for the user to adjust both 𝛾 and Ε, to the value that provides the best visualization of the mixture.…”

“…It is necessary to handle and examine substantial volumes of information. To achieve real-time performance without sacrificing detail, advanced rendering techniques are necessary [12][13][14][15]. This challenge is particularly evident in virtual reality platforms, which have strict graphics processing requirements that necessitate optimization.…”