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Density-Adaptive and Geometry-Aware Registration of TLS ...
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由 Y Zang 著作2019被引用 11 次 — Abstract: Probabilistic registration algorithms [e.g., coherent point drift, (CPD)] provide effective solutions for point cloud alignment.
Density-Adaptive and Geometry-Aware Registration of TLS Point ...
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A robust pairing method for two-pulse particle tracking velocimetry based on coherent point drift · Engineering, Physics. Measurement science and technology.
Density-Adaptive and Geometry-Aware Registration of TLS ...
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由 Y Zang 著作2019被引用 11 次 — Abstract—Probabilistic registration algorithms [e.g., coherent point drift, (CPD)] provide effective solutions for point cloud alignment.
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Density-Adaptive and Geometry-Aware Registration of TLS Point ...
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Density-Adaptive and Geometry-Aware Registration of TLS Point Clouds Based on Coherent Point Drift. Y. Zang, R. Lindenbergh, Bisheng Yang, H. Guan. 2020, IEEE ...
[PDF] Density Adaptive Point Set Registration
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The density-adaptive registration successfully handles severe density variations commonly encountered in terrestrial Lidar applications and outperforms ...
Density Adaptive Point Set Registration | Request PDF
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Density-Adaptive and Geometry-Aware Registration of TLS Point Clouds Based on Coherent Point Drift. Article. Nov 2019. Yufu Zang ...
Point Cloud Registration: Classic Approaches
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Coherent point drift is a probabilistic approach for the registration problen, which works on both rigid and non-rigid transforms. As the authors explain in the ...
GaussReg: Fast 3D Registration with Gaussian Splatting
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... Density-adaptive and geometry- aware registration of tls point clouds based on coherent point drift. IEEE Geo- science and Remote Sensing Letters 17(9) ...
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Optimization and validation of coherent point drift for planar-array ...
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Density-adaptive and geometry-aware registration of TLS point clouds based on coherent point drift. IEEE Geoscience and Remote Sensing Letters, 17, 1628 ...
GaussReg: Fast 3D Registration with Gaussian Splatting
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2024年7月7日 — We propose GaussReg, a novel coarse-to-fine framework, both fast and accurate. The coarse stage follows existing point cloud registration methods.