Octrees with near optimal cost for ray-shooting

Predicting and optimizing the performance of ray shooting is a very important problem in computer graphics due to the severe computational demands of ray tracing and other applications, e.g., radio propagation simulation. Aronov and Fortune were the first to guarantee an overall performance within a constant factor of optimal in the following model of computation: build a triangulation compatible with the scene, and shoot rays by locating origin and traversing until hit is found. Triangulations are not a very popular model in computer graphics, but space decompositions like kd-trees and octrees are used routinely. Aronov and coll. [1] developed a cost measure for such decompositions, and proved it to reliably predict the average cost of ray shooting. In this paper, we address the corresponding optimization problem on octrees with the same cost measure as the optimizing criterion. More generally, we solve the generalization for generalized octrees in any d dimensions with scenes made up of (d − 1)-dimensional simplices. We give a construction of trees which yields cost O(M), where M is the infimum of the cost measure on all trees. Sometimes, a balance condition is important (informally, balanced trees ensures that adjacent leaves have similar size): we also show that rebalancing does not affect the cost by more than a constant multiplicative factor. These are the first and only known results that provide performance guarantees on the approximation factor for 3dimensional ray shooting with this realistic model of computation. Our results have been validated experimentally by Aronov and coll. [2]

Automated Recognition of 3D CAD Model Objects in Dense Laser Range Point Clouds

There is shift in the Architectural / Engineering / Construction and Facility Management (AEC&FM) industry toward performance-driven projects. Assuring good performance requires efficient and reliable performance control processes. However, the current state of the AEC&FM industry is that control processes are inefficient because they generally rely on manually intensive, inefficient, and often inaccurate data collection techniques. Critical performance control processes include progress tracking and dimensional quality control. These particularly rely on the accurate and efficient collection of the as-built three-dimensional (3D) status of project objects. However, currently available techniques for as-built 3D data collection are extremely inefficient, and provide partial and often inaccurate information. These limitations have a negative impact on the quality of decisions made by project managers and consequently on project success. This thesis presents an innovative approach for Automated 3D Data Collection (A3dDC). This approach takes advantage of Laser Detection and Ranging (LADAR), 3D Computer-Aided-Design (CAD) modeling and registration technologies. The performance of this approach is investigated with a first set of experimental results obtained with real-life data. A second set of experiments then analyzes the feasibility of implementing, based on the developed approach, automated project performance control (APPC) applications such as automated project progress tracking and automated dimensional quality control. Finally, other applications are identified including planning for scanning and strategic scanning