R-trees for organizing and visualizing 3D GIS databases

Visibility Detection of 3D Objects and Visual K-Nearest Neighbor Query Based on Convex Hull Model

“…Shou et al [11] proposed an HDoV tree to improve the performance and visual fidelity of the visualization system. Kofler et al [12] combined R-tree and LOD and proposed a LOD-R tree index structure that could handle large amounts of data effectively. Zhang et al [13] discussed the problems of spatial query processing when obstacles existed and proposed an integrated framework that could solve these problems effectively.…”

“…(3) ListExitIndex � null; (4) for(k � 0; k < ListObs.size(); k++) do (5) if(!ListExitIndex.Contains(k)) then (6) ListExitIndex.Add(k); (7) obs � ListObs[k]; (8) end if (9) for(i � 0; i < ListObs.size(); i++) do (10) lines � getLines(obs); (11) face � getFace(ListObs[i]); (12) if(is rough(lines, face)) then (13) obs � ComObs (obs, listObs[i]); (14) ListExitIndex.Add(i); (15) end if (16) O′.Add(obs); (17) end for (18) end for (19) Return O′; projection point. Otherwise, we calculate the distance from the point to each vertex of the triangle.…”

“…e collision detection is performed between the "query point-object visual body" and the objects in the obstacle set (lines 5-7). If the "query point-object visual body based on the query object does not collide with any obstacle, it will be added to the result set and jump out of the current loop to continue the visibility detection of the next query object (lines [12][13][14]. If the collision occurs, the obstacle that collided with the obstacle candidate set ObsSet is added to judge whether the obstacle in ObsSet occludes the query object completely (line 15), if it does not occlude the current query object completely, jump out of the current loop, and judge the visibility of the next query object, otherwise jump out of the current loop directly and determine the visibility of the next query object (line [15][16][17].…”

“…(3) fac←getFac(Hull); (4) for each f in fac do (5) if(q.view(f ) � � true) then (6) discurl←dl(line, q, f ); (7) Discurls.Add(discurl); (8) end if (9) end for (10) Fac←GetFac(Concave); (7) for each ridge in Ridge do (8) for each fac in Fac do (9) if (ridge.id ! � fac.id) then (10) num←IntSegTri (ridge, fac); (11) end if (12) end for (13) end for (14) if num.Contains(1) then (15) Lis_Obs.Add(ObsSet [ridge.id]); (16) end if (17) end for (18) mulObs←MulJud (q, Obs, Lis_Obs); (19) TorF←OneJudVisible(q, Obj, mulObs); (20) 2) ddl←Sort(O, q); (3) for each Obj in ddl do ( 4) qToObj←Add(q); (5) for each Obs in ddl do (6) if Obs.address < Obj.address then (7) TorF←GJK(qToObj, Obs); (8) end if (9) if (TorF � � true) then (10) ObsSet.Add(Obs); (11) end if (12) if ObsSet.Count � � 0 then (13) Visiual.Add(qToObj); (14) else (15) if(Occlusion(q, Obj, ObsSet)) then ( 16)…”

Visibility Detection of 3D Objects and Visual K-Nearest Neighbor Query Based on Convex Hull Model

“…Shou et al [11] proposed an HDoV tree to improve the performance and visual fidelity of the visualization system. Kofler et al [12] combined R-tree and LOD and proposed a LOD-R tree index structure that could handle large amounts of data effectively. Zhang et al [13] discussed the problems of spatial query processing when obstacles existed and proposed an integrated framework that could solve these problems effectively.…”

“…(3) ListExitIndex � null; (4) for(k � 0; k < ListObs.size(); k++) do (5) if(!ListExitIndex.Contains(k)) then (6) ListExitIndex.Add(k); (7) obs � ListObs[k]; (8) end if (9) for(i � 0; i < ListObs.size(); i++) do (10) lines � getLines(obs); (11) face � getFace(ListObs[i]); (12) if(is rough(lines, face)) then (13) obs � ComObs (obs, listObs[i]); (14) ListExitIndex.Add(i); (15) end if (16) O′.Add(obs); (17) end for (18) end for (19) Return O′; projection point. Otherwise, we calculate the distance from the point to each vertex of the triangle.…”

“…e collision detection is performed between the "query point-object visual body" and the objects in the obstacle set (lines 5-7). If the "query point-object visual body based on the query object does not collide with any obstacle, it will be added to the result set and jump out of the current loop to continue the visibility detection of the next query object (lines [12][13][14]. If the collision occurs, the obstacle that collided with the obstacle candidate set ObsSet is added to judge whether the obstacle in ObsSet occludes the query object completely (line 15), if it does not occlude the current query object completely, jump out of the current loop, and judge the visibility of the next query object, otherwise jump out of the current loop directly and determine the visibility of the next query object (line [15][16][17].…”

“…(3) fac←getFac(Hull); (4) for each f in fac do (5) if(q.view(f ) � � true) then (6) discurl←dl(line, q, f ); (7) Discurls.Add(discurl); (8) end if (9) end for (10) Fac←GetFac(Concave); (7) for each ridge in Ridge do (8) for each fac in Fac do (9) if (ridge.id ! � fac.id) then (10) num←IntSegTri (ridge, fac); (11) end if (12) end for (13) end for (14) if num.Contains(1) then (15) Lis_Obs.Add(ObsSet [ridge.id]); (16) end if (17) end for (18) mulObs←MulJud (q, Obs, Lis_Obs); (19) TorF←OneJudVisible(q, Obj, mulObs); (20) 2) ddl←Sort(O, q); (3) for each Obj in ddl do ( 4) qToObj←Add(q); (5) for each Obs in ddl do (6) if Obs.address < Obj.address then (7) TorF←GJK(qToObj, Obs); (8) end if (9) if (TorF � � true) then (10) ObsSet.Add(Obs); (11) end if (12) if ObsSet.Count � � 0 then (13) Visiual.Add(qToObj); (14) else (15) if(Occlusion(q, Obj, ObsSet)) then ( 16)…”

A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

“…We hope 3D R-tree is a dynamic structure like R-tree, which is considered as a most promising spatial indexing method and has faster query efficiency than octree [30,31]. As 2D R*-tree does, 3D R*-tree also aims at improving query efficiency based on 3D R-tree by minimizing the overlaps, volume of the Minimum Bounding Box (MBB).…”

Visibility Detection of 3D Objects and Visual K-Nearest Neighbor Query Based on Convex Hull Model

“…Shou et al [11] proposed an HDoV tree to improve the performance and visual fidelity of the visualization system. Kofler et al [12] combined R-tree and LOD and proposed a LOD-R tree index structure that could handle large amounts of data effectively. Zhang et al [13] discussed the problems of spatial query processing when obstacles existed and proposed an integrated framework that could solve these problems effectively.…”

“…(3) ListExitIndex � null; (4) for(k � 0; k < ListObs.size(); k++) do (5) if(!ListExitIndex.Contains(k)) then (6) ListExitIndex.Add(k); (7) obs � ListObs[k]; (8) end if (9) for(i � 0; i < ListObs.size(); i++) do (10) lines � getLines(obs); (11) face � getFace(ListObs[i]); (12) if(is rough(lines, face)) then (13) obs � ComObs (obs, listObs[i]); (14) ListExitIndex.Add(i); (15) end if (16) O′.Add(obs); (17) end for (18) end for (19) Return O′; projection point. Otherwise, we calculate the distance from the point to each vertex of the triangle.…”

“…e collision detection is performed between the "query point-object visual body" and the objects in the obstacle set (lines 5-7). If the "query point-object visual body based on the query object does not collide with any obstacle, it will be added to the result set and jump out of the current loop to continue the visibility detection of the next query object (lines [12][13][14]. If the collision occurs, the obstacle that collided with the obstacle candidate set ObsSet is added to judge whether the obstacle in ObsSet occludes the query object completely (line 15), if it does not occlude the current query object completely, jump out of the current loop, and judge the visibility of the next query object, otherwise jump out of the current loop directly and determine the visibility of the next query object (line [15][16][17].…”

“…(3) fac←getFac(Hull); (4) for each f in fac do (5) if(q.view(f ) � � true) then (6) discurl←dl(line, q, f ); (7) Discurls.Add(discurl); (8) end if (9) end for (10) Fac←GetFac(Concave); (7) for each ridge in Ridge do (8) for each fac in Fac do (9) if (ridge.id ! � fac.id) then (10) num←IntSegTri (ridge, fac); (11) end if (12) end for (13) end for (14) if num.Contains(1) then (15) Lis_Obs.Add(ObsSet [ridge.id]); (16) end if (17) end for (18) mulObs←MulJud (q, Obs, Lis_Obs); (19) TorF←OneJudVisible(q, Obj, mulObs); (20) 2) ddl←Sort(O, q); (3) for each Obj in ddl do ( 4) qToObj←Add(q); (5) for each Obs in ddl do (6) if Obs.address < Obj.address then (7) TorF←GJK(qToObj, Obs); (8) end if (9) if (TorF � � true) then (10) ObsSet.Add(Obs); (11) end if (12) if ObsSet.Count � � 0 then (13) Visiual.Add(qToObj); (14) else (15) if(Occlusion(q, Obj, ObsSet)) then ( 16)…”

A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

“…We hope 3D R-tree is a dynamic structure like R-tree, which is considered as a most promising spatial indexing method and has faster query efficiency than octree [30,31]. As 2D R*-tree does, 3D R*-tree also aims at improving query efficiency based on 3D R-tree by minimizing the overlaps, volume of the Minimum Bounding Box (MBB).…”

Visibility Detection of 3D Objects and Visual K-Nearest Neighbor Query Based on Convex Hull Model

“…Shou et al [11] proposed an HDoV tree to improve the performance and visual fidelity of the visualization system. Kofler et al [12] combined R-tree and LOD and proposed a LOD-R tree index structure that could handle large amounts of data effectively. Zhang et al [13] discussed the problems of spatial query processing when obstacles existed and proposed an integrated framework that could solve these problems effectively.…”

“…(3) ListExitIndex � null; (4) for(k � 0; k < ListObs.size(); k++) do (5) if(!ListExitIndex.Contains(k)) then (6) ListExitIndex.Add(k); (7) obs � ListObs[k]; (8) end if (9) for(i � 0; i < ListObs.size(); i++) do (10) lines � getLines(obs); (11) face � getFace(ListObs[i]); (12) if(is rough(lines, face)) then (13) obs � ComObs (obs, listObs[i]); (14) ListExitIndex.Add(i); (15) end if (16) O′.Add(obs); (17) end for (18) end for (19) Return O′; projection point. Otherwise, we calculate the distance from the point to each vertex of the triangle.…”

“…e collision detection is performed between the "query point-object visual body" and the objects in the obstacle set (lines 5-7). If the "query point-object visual body based on the query object does not collide with any obstacle, it will be added to the result set and jump out of the current loop to continue the visibility detection of the next query object (lines [12][13][14]. If the collision occurs, the obstacle that collided with the obstacle candidate set ObsSet is added to judge whether the obstacle in ObsSet occludes the query object completely (line 15), if it does not occlude the current query object completely, jump out of the current loop, and judge the visibility of the next query object, otherwise jump out of the current loop directly and determine the visibility of the next query object (line [15][16][17].…”

“…(3) fac←getFac(Hull); (4) for each f in fac do (5) if(q.view(f ) � � true) then (6) discurl←dl(line, q, f ); (7) Discurls.Add(discurl); (8) end if (9) end for (10) Fac←GetFac(Concave); (7) for each ridge in Ridge do (8) for each fac in Fac do (9) if (ridge.id ! � fac.id) then (10) num←IntSegTri (ridge, fac); (11) end if (12) end for (13) end for (14) if num.Contains(1) then (15) Lis_Obs.Add(ObsSet [ridge.id]); (16) end if (17) end for (18) mulObs←MulJud (q, Obs, Lis_Obs); (19) TorF←OneJudVisible(q, Obj, mulObs); (20) 2) ddl←Sort(O, q); (3) for each Obj in ddl do ( 4) qToObj←Add(q); (5) for each Obs in ddl do (6) if Obs.address < Obj.address then (7) TorF←GJK(qToObj, Obs); (8) end if (9) if (TorF � � true) then (10) ObsSet.Add(Obs); (11) end if (12) if ObsSet.Count � � 0 then (13) Visiual.Add(qToObj); (14) else (15) if(Occlusion(q, Obj, ObsSet)) then ( 16)…”

A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

“…We hope 3D R-tree is a dynamic structure like R-tree, which is considered as a most promising spatial indexing method and has faster query efficiency than octree [30,31]. As 2D R*-tree does, 3D R*-tree also aims at improving query efficiency based on 3D R-tree by minimizing the overlaps, volume of the Minimum Bounding Box (MBB).…”