PPT-PLOC++ Parallel Locally-Ordered Clustering for Bounding Volume Hierarchy Construction

Carsten Benthin Radoslaw Drabinski Lorenzo Tessari Addis Dittebrandt Intel Corporation Motivation Raytracing has become mainstream for interactiverealtime apps Bounding Volume Hierarchy BVH dominant acceleration structure. Collisions. Collisions. Detection. Broad Phase. Bounding Volumes. Key idea:. Surround the object with a (simpler) bounding object (the bounding volume).. If something does not collide with the bounding volume, it does not collide with the object inside.. Collision Detection. Probabilistic Roadmaps. How to test for. collision?. Collision Detection Methods. Many different methods. In particular:. Grid method. : good for many simple moving objects of about the same size (e.g., many moving discs with similar radii). Strong NE Flow converged along the NE Florida coast following the passage of a slow moving backdoor front. . There was adequate instability across the forecast area to support convection…but a trigger was need to initiate shower activity. . Dr Susan Cartwright. Dept of Physics and Astronomy. University of Sheffield. Parallel Universes. Are you unique?. Could there be another “you” differing only in what you had for breakfast this morning?. Samuli . Laine. Tero Karras. NVIDIA. The Problem. How to quickly find a good bounding plane with a given orientation?. Use Case 1: Ray Tracing and Rigid Motion. Say we have a world-space BVH with leaves containing object ID transformation matrix. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . to . LC-MS Data Analysis.  . October 7 2013. . IEEE . International Conference on Big Data 2013 (IEEE . BigData. 2013. ). Santa Clara CA. Geoffrey Fox, D. R. Mani, . Saumyadipta. . Pyne. gcf@indiana.edu. What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Kris Hauser. I400/B659: Intelligent Robotics. Spring 2014. 3D models in robotics. Design. Simulation. Robot collision detection (i.e. prediction). Proximity calculation. Map building. Object recognition. Ordered Choices. Ordered Discrete Outcomes. E.g.: Taste test, credit rating, course grade, preference scale. Underlying random preferences: . Existence of an underlying continuous preference scale. Mapping to observed choices. 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. LIBA’s Strategic Plan ( 2017 - 2021 ) Approved by a majority vote of the board of directors on 6/14/17. Our Vision Louisville is an authentic, unique, thriving community where everyone considers What is clustering?. Grouping set of documents into subsets or clusters.. The Goal of clustering algorithm is:. To create clusters that are coherent internally, but clearly different from each other. By:. Dasari Charithambika (210302). Divya Gupta(210353). Course Instructors:. Dr. Preeti Malakar. Dr. Soumya Dutta.. M. Larsen, S. Labasan, P. Navrátil,. J.S. Meredith, and H. Childs (2015). Various hardware architectures are used in supercomputers, including GPUs, many-core coprocessors, large multi-core CPUs, low-power architectures, hybrid designs, and experimental designs..