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3D Complexity

by: Alayna Veum
Alayna Veum

GPA 3.81

Jaroslaw Rossignac

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About this Document

Jaroslaw Rossignac
Class Notes
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This 0 page Class Notes was uploaded by Alayna Veum on Monday November 2, 2015. The Class Notes belongs to CS 7491 at Georgia Institute of Technology - Main Campus taught by Jaroslaw Rossignac in Fall. Since its upload, it has received 12 views. For similar materials see /class/234009/cs-7491-georgia-institute-of-technology-main-campus in ComputerScienence at Georgia Institute of Technology - Main Campus.

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Date Created: 11/02/15
Professor J arek cs749l Class Notes 2006110 Course Contact Information WWW gvu gatechedu j areldcourses 749 l j arekccgatechedu On Complexity Complexity means trouble Why complexity can make algorithms impracticably slow things that are complex cannot easily be rendered in realtime transmission delays transmitting complex things requires lots of bandwidth pagingcaching issues complex things require lots of memory visual clutter complex scenes and diagrams are difficult to understand mathematically challenging complex things make our heads hurt complexity leads to unreliable software complex code is bugprone Our goal is to reduce the complexity of the things we work With Broad Principles for Fighting Complexity remove redundant information 7 example backface culling Replace or approximate complex components With simple ones 7 example level of detail replacing a circle with a decagon change the paradigm 7 example Image Based Rendering 1BR instead of polygonal based precompute complex things 7 examples complex animations occlusion visibility testing conservative tests 7 example using bounding boxesspheres for collision detection Complexity Types Algebraic degree of polynomial 7 example highorder polynomial surfaces cones toruses 7 solution replace a torus With a polyhedron Tupulugml see ganus nunrrramfuld ganusxsthenumba39uf es39 era mesh example amms ganus 1 15 lupulugmlly mum eemplexmana whats ganus U Mexphelegaushapermtures smeeumess nr sharpness era srape huw mkyquottheshapexs megmar skapes Enbe eensmaed mmphulugmlly cumplEX murmu nmcszsmwme murmummeaummmex Cnmbmalunal example mangleeeumera mesh Represemahunal represmung cumplEX data is mmeuu relates m msual clutter szml example dep39hcumplex ny shapes thalane cumEx favenu depm crumplth shapes39halhav many cunmve pans havehlgh aepm crumplth uw new eummexw lt i lt w s mgh depth LummeXW Dyname ammauun example meets sueh as Walls buddmg under pressure meme mlculated m ml ume Emma Seems pm 31 web pageath sLLAmgALgaLmhmJzjmsm Quanuzauun Stalng BEIan deumeneaurrgpmm numbers mm rmegers 12 bus Lhsxsalussy cumpressmerensmnxslust Replace sueuegy lmmr premenerr replacex yo wALh xrxu yr7yrr Lhs 15 ml lussy bum duem lnecesanly get us much W1 Premener rSL urdEr r eplaee x y wnh x 7 er xrr 7 m y 7 yr yrr 7 yrs reeerus me differmce belwem me predmmd rm pmm and me gem ea perm Premener mgher erder use srmemmg techmques based un me nergqbers erpmms m mere accumldy predld me lumuuns er subsequem perms me a dmmns and ffermcesbelwem 39hepre mammal shemdbe smaner here man mmg rsburder premenerr makmg me resuus 513 w eempress s emer uremeuen Hmmmer premeuen gt v w va v3 v8 Slum use 3 Me curve rs rearranged 5 at all perms are equldls39anl mm each emer men my me angle mm une pmm m thenm x5211 that needs m be stared Mum mum Quanuzauun rermma mlega39fml x 7 mund x7x n71 x e x Where ms menumber ufbxls m ammeger Prufessnrlarekrecummends usmg n12 as a starting point in the project Note that when quantizing in many dimensions compute the unit size how big in the original real number base each integer value is along the largest dimension It is recommended that this unit size be consistent for all dimensions After quantizing in this manner the differences between neighboring points will hopefully be very small Because these differences are integers highorder bits that are never used can be discarded


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