Enhanced Visualization of Detected 3D Geometric Differences

Abstract

The wide availability of 3D acquisition devices makes viable their use for shape monitoring. The current techniques for the analysis of time‐varying data can efficiently detect actual significant geometric changes and rule out differences due to irrelevant variations (such as sampling, lighting and coverage). On the other hand, the effective visualization of such detected changes can be challenging when we want to show at the same time the original appearance of the 3D model. In this paper, we propose a dynamic technique for the effective visualization of detected differences between two 3D scenes. The presented approach, while retaining the original appearance, allows the user to switch between the two models in a way that enhances the geometric differences that have been detected as significant. Additionally, the same technique is able to visually hides the other negligible, yet visible, variations. The main idea is to use two distinct screen space time‐based interpolation functions for the significant 3D differences and for the small variations to hide. We have validated the proposed approach in a user study on a different class of datasets, proving the objective and subjective effectiveness of the method.

Spatial change detection using normal distributions transform

“…In general, spatial change detection can be classified into three groups: point/mesh-based, height-based, and voxel-based comparisons. Point/mesh-based comparison [6,19] is a technique that compares the distance of nearest points or meshes in two point clouds, which is similar to the ICP algorithm [20]. Lague et al [4] proposed the use of the distance along normal direction of a local surface to make the algorithm robust to errors in 3D terrain data measured by a terrestrial laser scanner.…”

“…Spatial change detection is a fundamental technique for finding the differences between two or more pieces of geometrical information. This technique is indispensable in several applications, such as topographic change detection in airborne laser scanning [1,2] or terrestrial laser scanning [3,4], map maintenance in urban areas [5], preservation of cultural heritage [6], and analysis of plant growth [7]. In robotics, the detection of spatial changes around a robot is often used in some applications, such as daily service, search and rescue, security, and surveillance.…”

Visualization Method for Porous Groundwater Seepage Flow Field Based on Particle Flow: Case of Yancheng City in the East Coast of China

“…Flow visualization can be divided into four categories in accordance with visual expression: geometric visualization [9,10], volume visualization [11,12], texture-based visualization [13], and feature-based visualization [14]. Geometric shape visualization is relatively simple, intuitive, and easy to understand; thus, it is the most widely used visualization method in engineering [15].…”