IEEE Trans Vis Comput Graph - Multi-Resolution Attributes for Hardware Tessellated Objects.

Tópicos

{ structur(1116) can(940) graph(676) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ concept(1167) ontolog(924) domain(897) }
{ method(984) reconstruct(947) comput(926) }
{ imag(2830) propos(1344) filter(1198) }
{ algorithm(1844) comput(1787) effici(935) }
{ visual(1396) interact(850) tool(830) }
{ measur(2081) correl(1212) valu(896) }
{ featur(3375) classif(2383) classifi(1994) }
{ data(1714) softwar(1251) tool(1186) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ sampl(1606) size(1419) use(1276) }
{ health(1844) social(1437) communiti(874) }
{ estim(2440) model(1874) function(577) }
{ imag(1947) propos(1133) code(1026) }
{ method(1219) similar(1157) match(930) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
{ extract(1171) text(1153) clinic(932) }
{ care(1570) inform(1187) nurs(1089) }
{ featur(1941) imag(1645) propos(1176) }
{ research(1085) discuss(1038) issu(1018) }
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{ cost(1906) reduc(1198) effect(832) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ can(981) present(881) function(850) }
{ result(1111) use(1088) new(759) }
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{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
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{ framework(1458) process(801) describ(734) }
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{ error(1145) method(1030) estim(1020) }
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{ learn(2355) train(1041) set(1003) }
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{ search(2224) databas(1162) retriev(909) }
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{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Hardware tessellation is one of the latest GPU features. Triangle or quad meshes are tessellated on-the-fly, where the tessellation level is chosen adaptively in a separate shader. The hardware tessellator only generates topology; attributes such as positions or texture coordinates of the newly generated vertices are determined in a domain shader. Typical applications of hardware tessellation are view dependent tessellation of parametric surfaces and displacement mapping. Often, the attributes for the newly generated vertices are stored in textures, which requires uv unwrapping, chartification, and atlas generation of the input mesh - a process that is time consuming and often requires manual intervention. In this paper, we present an alternative representation that directly stores optimized attribute values for typical hardware tessellation patterns and simply assigns these attributes to the generated vertices at render time. Using a multi-level fitting approach, the attribute values are optimized for several resolutions. Thereby, we require no parameterization, save memory by adapting the density of the samples to the content, and avoid discontinuities by construction.

Resumo Limpo

hardwar tessel one latest gpu featur triangl quad mesh tessel onthefli tessel level chosen adapt separ shader hardwar tessel generat topolog attribut posit textur coordin newli generat vertic determin domain shader typic applic hardwar tessel view depend tessel parametr surfac displac map often attribut newli generat vertic store textur requir uv unwrap chartif atlas generat input mesh process time consum often requir manual intervent paper present altern represent direct store optim attribut valu typic hardwar tessel pattern simpli assign attribut generat vertic render time use multilevel fit approach attribut valu optim sever resolut therebi requir parameter save memori adapt densiti sampl content avoid discontinu construct

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