Overview of Shelling for 2-Manifold Surface Reconstruction Based on 3D Delaunay Triangulation

Lhuillier, Maxime

doi:10.1007/s10851-017-0734-4

Cited by 3 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the boundary of O) forms a manifold surface. Visual artifacts of [22] are corrected by [25] and are limited by [32], but the artifact problem is not completely solved ( [20]). The visibility is partly ignored by [8] since the inside grows in the matter.…”

Section: Manifoldnessmentioning

confidence: 99%

“…Local extrema are partly due to limitations of an operation called "Shelling", which adds one tetrahedron at once to the growing set O. Although it provides most tetrahedra in O, it can "get stuck" in unexpected cases ( [20,47]). We propose a method based on the new repair to escape from local extrema.…”

Section: Our Contributionsmentioning

confidence: 99%

“…However, Sh. is not sufficient since it cannot change g ( [20]), the genus of ∂O. If the genus of the true surface is not 0, e.g.…”

Section: Principlesmentioning

confidence: 99%

“…and we set O = {∆} with a tetrahedron ∆ ∈ O . According to [47,20], there are three cases. Case 1: Shelling can success and can fail (This depends on its tetrahedron choices.…”

Section: Examples Of Shelling Blockingmentioning

confidence: 99%

“…In this example, O ∪ P is a cylinder with an octagonal base.14:if(R sub > R add ) // abandon if f (O) decreases 15: { O ← O \ L add ; O ← O ∪ L sub ; } 16: } else O ← O ∪ L sub ; // abandon if ∂O is not manifold 17: } endAppendix E. Shelling blocking using five tetrahedra Let P ⊆ F be defined (as in Sec. 6.1 of[20]) by{a 0 a 1 b 1 c 0 , a 0 a 1 b 0 c 0 , a 1 b 0 b 1 c 0 , a 0 a 1 b 1 c 1 , a 0 b 1 c 0 c 1 } (E.1)using distinct vertices a 0 , a 1 , b 0 , b 1 , c 0 and c 1 . We summarize P: it has one "internal" tetrahedron a 0 a 1 b 1 c 0 since every triangle face of a 0 a 1 b 1 c 0 is included in another tetrahedron of P having vertex b 0 or c 1 .…”

mentioning

confidence: 99%

See 4 more Smart Citations

Surface reconstruction from a sparse point cloud by enforcing visibility consistency and topology constraints

Lhuillier

2018

Computer Vision and Image Understanding

Self Cite

View full text Add to dashboard Cite

Highlights• Surface reconstruction under-uses topology constraints in Computer Vision• Start from a previous method enforcing manifoldness on a sparse point cloud• Simultaneously enforce visibility consistency and low genus for the first time• Improve the removal of surface singularities and the escape from local extrema• Experiment on long video sequences taken by a helmetheld omnidirectional camera AbstractThere are reasons to reconstruct a surface from a sparse cloud of 3D points estimated from an image sequence: to avoid computationally expensive dense stereo, e.g. for applications that do not need high level of details and have limited resources, or to initialize dense stereo in other cases. It is also interesting to enforce topology constraints (like manifoldness) for both surface regularization and applications. In this article, we improve by several ways a previous method that enforces the manifold constraint given a sparse point cloud. We enforce lowered genus, i.e. simplified topology, as a further regularization constraint for maximizing the visibility consistency encoded in a 3D Delaunay triangulation of the points. We also provide more efficient escapes from local extrema, an acceleration of the manifold test and more efficient removals of surface singularities. We experiment on a sparse point cloud reconstructed from videos, that are taken by a helmet-held omnidirectional multi-camera moving in an university campus.

show abstract

Section: Manifoldnessmentioning

confidence: 99%

Section: Our Contributionsmentioning

confidence: 99%

“…However, Sh. is not sufficient since it cannot change g ( [20]), the genus of ∂O. If the genus of the true surface is not 0, e.g.…”

Section: Principlesmentioning

confidence: 99%

“…and we set O = {∆} with a tetrahedron ∆ ∈ O . According to [47,20], there are three cases. Case 1: Shelling can success and can fail (This depends on its tetrahedron choices.…”

Section: Examples Of Shelling Blockingmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Surface reconstruction from a sparse point cloud by enforcing visibility consistency and topology constraints

Lhuillier

2018

Computer Vision and Image Understanding

Self Cite

View full text Add to dashboard Cite

show abstract

DEM Construction Method for Slopes Using Three-Dimensional Point Cloud Data Based on Moving Least Square Theory

Wang

Liao

2020

J. Surv. Eng.

View full text Add to dashboard Cite

Ultrasonic localization method based on receiver array optimization schemes

Luo

Hu-cheng

Yan

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Ultrasonic localization system using trilateration has been adopted in various types of full-enclosed and semi-enclosed space. For small-scale or enclosed scenarios, there is an important phenomenon, that is, the ultrasonic wave may rapidly reflect back and forth between the walls to form different normal waves during the process of propagation, which may break the inverse square law of sound intensity (the sound intensity in free space is inversely proportional to the square of the distance). The other problem is that the sound intensity of some local corner points are obviously stronger than those points near the acoustic source, which may confuse the ultrasonic receivers and bring significant differences in measurement errors. To avoid excessive errors influence for the positioning accuracy, in this article, a novel ultrasonic localization method based on receiver array optimization schemes is reported. The main purpose is to activate the ultrasonic receiver in the area as well as shield the ultrasonic receiver outside the area. The system error will be reduced based on this method, and most of the redundant data will be eliminated. The simulation results are visualized and compared with the existing methods. The positioning accuracy measured by our method is increased significantly.

show abstract

Overview of Shelling for 2-Manifold Surface Reconstruction Based on 3D Delaunay Triangulation

Cited by 3 publications

References 19 publications

Surface reconstruction from a sparse point cloud by enforcing visibility consistency and topology constraints

Surface reconstruction from a sparse point cloud by enforcing visibility consistency and topology constraints

DEM Construction Method for Slopes Using Three-Dimensional Point Cloud Data Based on Moving Least Square Theory

Ultrasonic localization method based on receiver array optimization schemes

Contact Info

Product

Resources

About