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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2412.11680 |
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| _version_ | 1866917870100807680 |
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| author | Fang, Zheng Ye, Ke Liu, Yaofang Li, Gongzhe Zhao, Xianhong Li, Jialong Wang, Ruxin Zhang, Yuchen Ji, Xiangyang Sun, Qilin |
| author_facet | Fang, Zheng Ye, Ke Liu, Yaofang Li, Gongzhe Zhao, Xianhong Li, Jialong Wang, Ruxin Zhang, Yuchen Ji, Xiangyang Sun, Qilin |
| contents | Point clouds or depth images captured by current RGB-D cameras often suffer from low resolution, rendering them insufficient for applications such as 3D reconstruction and robots. Existing point cloud super-resolution (PCSR) methods are either constrained by geometric artifacts or lack attention to edge details. To address these issues, we propose an edge-guided geometric-preserving 3D point cloud super-resolution (EGP3D) method tailored for RGB-D cameras. Our approach innovatively optimizes the point cloud with an edge constraint on a projected 2D space, thereby ensuring high-quality edge preservation in the 3D PCSR task. To tackle geometric optimization challenges in super-resolution point clouds, particularly preserving edge shapes and smoothness, we introduce a multi-faceted loss function that simultaneously optimizes the Chamfer distance, Hausdorff distance, and gradient smoothness. Existing datasets used for point cloud upsampling are predominantly synthetic and inadequately represent real-world scenarios, neglecting noise and stray light effects. To address the scarcity of realistic RGB-D data for PCSR tasks, we built a dataset that captures real-world noise and stray-light effects, offering a more accurate representation of authentic environments. Validated through simulations and real-world experiments, the proposed method exhibited superior performance in preserving edge clarity and geometric details. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11680 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera Fang, Zheng Ye, Ke Liu, Yaofang Li, Gongzhe Zhao, Xianhong Li, Jialong Wang, Ruxin Zhang, Yuchen Ji, Xiangyang Sun, Qilin Computer Vision and Pattern Recognition Point clouds or depth images captured by current RGB-D cameras often suffer from low resolution, rendering them insufficient for applications such as 3D reconstruction and robots. Existing point cloud super-resolution (PCSR) methods are either constrained by geometric artifacts or lack attention to edge details. To address these issues, we propose an edge-guided geometric-preserving 3D point cloud super-resolution (EGP3D) method tailored for RGB-D cameras. Our approach innovatively optimizes the point cloud with an edge constraint on a projected 2D space, thereby ensuring high-quality edge preservation in the 3D PCSR task. To tackle geometric optimization challenges in super-resolution point clouds, particularly preserving edge shapes and smoothness, we introduce a multi-faceted loss function that simultaneously optimizes the Chamfer distance, Hausdorff distance, and gradient smoothness. Existing datasets used for point cloud upsampling are predominantly synthetic and inadequately represent real-world scenarios, neglecting noise and stray light effects. To address the scarcity of realistic RGB-D data for PCSR tasks, we built a dataset that captures real-world noise and stray-light effects, offering a more accurate representation of authentic environments. Validated through simulations and real-world experiments, the proposed method exhibited superior performance in preserving edge clarity and geometric details. |
| title | EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.11680 |