An easy viewer for out-of-core visualization of huge point-sampled models

Meng, Fengyan; Zha, Hongbin

doi:10.1109/tdpvt.2004.1335196

Cited by 3 publications

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Simplification of Fan-Meshes Models for Fast Rendering of Large 3D Point-Sampled Scenes

Xiaotian

Meng

Zha

2005

Image Analysis and Processing – ICIAP 2005

View full text Add to dashboard Cite

Abstract. Fan-Meshes (FM) are a kind of geometrical primitives for generating 3D model or scene descriptions that are able to preserve both local geometrical details and topological structures. In this paper, we propose an efficient simplification algorithm for the FM models to achieve fast post-processing and rendering of large models or scenes. Given a global error tolerance for the surface approximation, the algorithm can find an approximately minimal set of FMs that covers the whole model surfaces. As compared with splat-based methods, the FM description has a large simplification rate under the same surface fitting error measurement.

show abstract

Simplification of Fan-Meshes Models for Fast Rendering of Large 3D Point-Sampled Scenes

Xiaotian

Meng

Zha

2005

Image Analysis and Processing – ICIAP 2005

View full text Add to dashboard Cite

show abstract

Open Source First Person View 3D Point Cloud Visualizer for Large Data Sets

Palha

Murtiyoso

Michelin

et al. 2016

Lecture Notes in Geoinformation and Cartography

View full text Add to dashboard Cite

Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry

Murtiyoso

Pellis

Grussenmeyer

et al. 2022

Sensors

View full text Add to dashboard Cite

Developments in the field of artificial intelligence have made great strides in the field of automatic semantic segmentation, both in the 2D (image) and 3D spaces. Within the context of 3D recording technology it has also seen application in several areas, most notably in creating semantically rich point clouds which is usually performed manually. In this paper, we propose the introduction of deep learning-based semantic image segmentation into the photogrammetric 3D reconstruction and classification workflow. The main objective is to be able to introduce semantic classification at the beginning of the classical photogrammetric workflow in order to automatically create classified dense point clouds by the end of the said workflow. In this regard, automatic image masking depending on pre-determined classes were performed using a previously trained neural network. The image masks were then employed during dense image matching in order to constraint the process into the respective classes, thus automatically creating semantically classified point clouds as the final output. Results show that the developed method is promising, with automation of the whole process feasible from input (images) to output (labelled point clouds). Quantitative assessment gave good results for specific classes e.g., building facades and windows, with IoU scores of 0.79 and 0.77 respectively.

show abstract

An easy viewer for out-of-core visualization of huge point-sampled models

Abstract: In this paper, we propose a viewer for huge point-sampled models by combining out-of-core technologies with view-dependent level-of-detail (LOD)

Cited by 3 publications

References 23 publications

Simplification of Fan-Meshes Models for Fast Rendering of Large 3D Point-Sampled Scenes

Simplification of Fan-Meshes Models for Fast Rendering of Large 3D Point-Sampled Scenes

Open Source First Person View 3D Point Cloud Visualizer for Large Data Sets

Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry

Contact Info

Product

Resources

About