DeepDT: Learning Geometry From Delaunay Triangulation for Surface Reconstruction

Luo, Yiming; Mi, Zhenxing; Tao, Wenbing

doi:10.1609/aaai.v35i3.16327

Cited by 23 publications

(7 citation statements)

References 38 publications

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“…This dataset finds wide applications in fields like 3D reconstruction, SLAM, map creation, and robot navigation. Moreover, we employ the Stanford [ 15 ] standard dataset to verify our model’s recognition performance. This dataset, commonly used for more demanding semantic scene segmentation tasks, consists of six indoor areas with 2.79 billion points scanned from 271 rooms across three different buildings.…”

Section: Resultsmentioning

confidence: 99%

“…Our method involves using point cloud data that combines 3D positional information (XYZ) and 2D color space information (RGB) [ 12 ] as input, thereby integrating a multi-modal information point cloud structure. We apply our proposed MInet to the standard ShapeNet dataset [ 13 ] and the complex environment dataset, ThreeDMatch [ 14 ], for segmentation tasks, and the Stanford3D [ 15 ] dataset for recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

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MInet: A Novel Network Model for Point Cloud Processing by Integrating Multi-Modal Information

Wang

Zuo

et al. 2023

Sensors

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Three-dimensional LiDAR systems that capture point cloud data enable the simultaneous acquisition of spatial geometry and multi-wavelength intensity information, thereby paving the way for three-dimensional point cloud recognition and processing. However, due to the irregular distribution, low resolution of point clouds, and limited spatial recognition accuracy in complex environments, inherent errors occur in classifying and segmenting the acquired target information. Conversely, two-dimensional visible light images provide real-color information, enabling the distinction of object contours and fine details, thus yielding clear, high-resolution images when desired. The integration of two-dimensional information with point clouds offers complementary advantages. In this paper, we present the incorporation of two-dimensional information to form a multi-modal representation. From this, we extract local features to establish three-dimensional geometric relationships and two-dimensional color relationships. We introduce a novel network model, termed MInet (Multi-Information net), which effectively captures features relating to both two-dimensional color and three-dimensional pose information. This enhanced network model improves feature saliency, thereby facilitating superior segmentation and recognition tasks. We evaluate our MInet architecture using the ShapeNet and ThreeDMatch datasets for point cloud segmentation, and the Stanford dataset for object recognition. The robust results, coupled with quantitative and qualitative experiments, demonstrate the superior performance of our proposed method in point cloud segmentation and object recognition tasks.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MInet: A Novel Network Model for Point Cloud Processing by Integrating Multi-Modal Information

Wang

Zuo

et al. 2023

Sensors

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“…Voronoi Tessellation and Delaunay Triangulation have been used in a practical and theoretical way in many fields, such as science, technology and visual art [ 26 , 27 , 28 , 29 , 30 , 31 ]. Given a set of seed points in the plane, their Voronoi Tessellation divides the plane according to the nearest-neighbor rule [ 32 ].…”

Section: Proposed Methodologiesmentioning

confidence: 99%

DTTrans: PV Power Forecasting Using Delaunay Triangulation and TransGRU

Song

Jeong

Moon

et al. 2022

Sensors

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In an era of high penetration of renewable energy, accurate photovoltaic (PV) power forecasting is crucial for balancing and scheduling power systems. However, PV power output has uncertainty since it depends on stochastic weather conditions. In this paper, we propose a novel short-term PV forecasting technique using Delaunay triangulation, of which the vertices are three weather stations that enclose a target PV site. By leveraging a Transformer encoder and gated recurrent unit (GRU), the proposed TransGRU model is robust against weather forecast error as it learns feature representation from weather data. We construct a framework based on Delaunay triangulation and TransGRU and verify that the proposed framework shows a 7–15% improvement compared to other state-of-the-art methods in terms of the normalized mean absolute error. Moreover, we investigate the effect of PV aggregation for virtual power plants where errors can be compensated across PV sites. Our framework demonstrates 41–60% improvement when PV sites are aggregated and achieves as low as 3–4% of forecasting error on average.

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“…The locality makes the method scale to large scenes. The method of Luo et al [54] proceeds similarly, but without the use of visibility information and a global energy formulation. Instead, the GNN processes the 3DT of entire objects at once, which can hamper scalability.…”

Section: Interpolating Approachesmentioning

confidence: 99%

A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds

Sulzer¹,

Landrieu²,

Marlet³

et al. 2023

Preprint

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We survey and benchmark traditional and novel learning-based algorithms that address the problem of surface reconstruction from point clouds. Surface reconstruction from point clouds is particularly challenging when applied to real-world acquisitions, due to noise, outliers, non-uniform sampling and missing data. Traditionally, different handcrafted priors of the input points or output surface have been proposed to make the problem more tractable. However, hyperparameter tuning for adjusting priors to different acquisition defects can be a tedious task. To this end, the deep learning community has recently addressed the surface reconstruction problem. In contrast to traditional approaches, deep surface reconstruction methods can learn priors directly from a training set of point clouds and corresponding true surfaces. In our survey, we detail how different handcrafted and learned priors affect the robustness of methods to defect-laden input and their capability to generate geometric and topologically accurate reconstructions. In our benchmark, we evaluate the reconstructions of several traditional and learning-based methods on the same grounds. We show that learning-based methods can generalize to unseen shape categories, but their training and test sets must share the same point cloud characteristics. We also provide the code and data to compete in our benchmark and to further stimulate the development of learning-based surface reconstruction: https://github.com/raphaelsulzer/dsr-benchmark.

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DeepDT: Learning Geometry From Delaunay Triangulation for Surface Reconstruction

Cited by 23 publications

References 38 publications

MInet: A Novel Network Model for Point Cloud Processing by Integrating Multi-Modal Information

MInet: A Novel Network Model for Point Cloud Processing by Integrating Multi-Modal Information

DTTrans: PV Power Forecasting Using Delaunay Triangulation and TransGRU

A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds

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