Open3D

Lü, Zhihan; Guerrero, Paul; Mitra, Niloy J.; Steed, Anthony

doi:10.1145/2945292.2945302

Cited by 15 publications

(2 citation statements)

References 22 publications

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“…From the results of this experiment, we can find the optimal parameter for the filter. Our experiments were conducted with the open3D package [37] from the Python language. A workstation with an Intel® Xeon® Silver 4114 CPU @2.20 GHz, GRID Virtual GPU V100D-8Q, and 32 GB RAM memory was utilized in our experiment.…”

Section: Comparison Of Outlier Filtersmentioning

confidence: 99%

An Adaptive Group of Density Outlier Removal Filter: Snow Particle Removal from LiDAR Data

Cheng

Liu

et al. 2022

Electronics

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Light Detection And Ranging (LiDAR) is an important technology integrated into self-driving cars to enhance the reliability of these systems. Even with some advantages over cameras, it is still limited under extreme weather conditions such as heavy rain, fog, or snow. Traditional methods such as Radius Outlier Removal (ROR) and Statistical Outlier Removal (SOR) are limited in their ability to detect snow points in LiDAR point clouds. This paper proposes an Adaptive Group of Density Outlier Removal (AGDOR) filter that can remove snow particles more effectively in raw LiDAR point clouds, with verification on the Winter Adverse Driving Dataset (WADS). In our proposed method, an intensity threshold combined with a proposed outlier removal filter was employed. Outstanding performance was obtained, with higher accuracy up to 96% and processing speed of 0.51 s per frame in our result. In particular, our filter outperforms the state-of-the-art filter by achieving a 16.32% higher Precision at the same accuracy. However, our method archive is lower in recall than the state-of-the-art method. This clearly indicates that AGDOR retains a significant amount of object points from LiDAR. The results suggest that our filter would be useful for snow removal under harsh weathers for autonomous driving systems.

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Section: Comparison Of Outlier Filtersmentioning

confidence: 99%

An Adaptive Group of Density Outlier Removal Filter: Snow Particle Removal from LiDAR Data

Cheng

Liu

et al. 2022

Electronics

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“…(Kim et al 2017) presented a visual extension of Cesium to visualize moving objects in a space-time cube. (Lu, Guerrero, Mitra & Steed, 2016) implemented an online 3D city model interactive editor using a light-weight viewer based on Cesium.…”

Section: Related Workmentioning

confidence: 99%

Interoperable Visualization of 3d City Models Using Ogc’s Standard 3d Portrayal Service

Koukofikis

Coors

Gutbell

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The demand of serving large 3D spatial data, mainly of urban areas, reflects the need of hierarchical data structures for 3D data. During the last years the OGC community standard I3S (Indexed 3d Scene Layer, ESRI) and 3D Tiles (Analytical Graphics, Inc.) emerged in order to deal with this challenge. Conceptually, hierarchical structures for 3D data operate similarly to web map tiles, differing only in the implementation. Although 3D hierarchical formats can transmit arbitrary sized geospatial data, they are not interoperable with consuming/visualization technologies on the client. A series of prototype implementations focus on rendering of hierarchical organized massive 3D data in various web client technologies employing the 3D Portrayal Service. As a result, the user can query a scene via the 3D Portrayal Service by specifying a spatial region, rather than a specific resource via a URI. The result is delivered either using I3S or 3D Tiles as a data delivery format, depending on which data is available for the specified region. The client APIs are capable of rendering either the I3S or the 3D Tiles content.</p>

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Parallel surface reconstruction for large‐scale scenes in the wild

Cao

Gao

Jia

2021

Circuit Theory & Apps

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Recovering a high‐quality texture model in a short time still a pursued goal in both computer vision and graphics communities. Thus, in the past decade, several methods have been proposed for fitting a texture model from the dense point clouds. However, these methods are computational intensity and also suffer from noise especially in outdoor. Moreover, with the popularization of Unmanned Aerial Vehicles (UAVs), it is getting easier to capture image data. While modern methods have much novelty, they may spend a long time on big image datasets. To accelerate the process of texture modeling, in this paper we present a parallel approach to fitting a texture model from the dense point clouds. The presented method makes use of the parallel computing technology and is implemented in parallel octree structure as well as parallel marching cubes. Finally, we conduct a comprehensive experiment on several benchmarking datasets and experimental results show that our method outperforms the state‐of‐the‐art methods and has also a 20 times acceleration.

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Open3D

Cited by 15 publications

References 22 publications

An Adaptive Group of Density Outlier Removal Filter: Snow Particle Removal from LiDAR Data

An Adaptive Group of Density Outlier Removal Filter: Snow Particle Removal from LiDAR Data

Interoperable Visualization of 3d City Models Using Ogc’s Standard 3d Portrayal Service

Parallel surface reconstruction for large‐scale scenes in the wild

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