Search

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.

The correct understanding of the 3D shape is a crucial aspect to improve the 3D scanning process, especially in order to perform high quality and as complete as possible 3D acquisitions on the field. The paper proposes a new technique to enhance the visualization of raw scanning data based on the definition in device space of a Multi-View Ambient Occlusion (MVAO). The approach allows improving the comprehension of the 3D shape of the input geometry and, requiring almost no preprocessing, it can be directly applied to raw captured point clouds. The algorithm has been tested on different datasets: high resolution Time-of-Flight scans and streams of low quality range maps from a depth camera. The results enhance the details perception in the 3D geometry using the multi-view information to make more robust the ambient occlusion estimation